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Tomas Pfister

Tomas Pfister

Tomas Pfister is the Head of Cloud AI Research. He came to Google from Apple where he cofounded Apple's central AI research group and published Apple’s first research paper that won the Best Paper Award at CVPR’17. Tomas’ key scientific achievements have been proposing a method to improve the realism of synthetic images; developing the first automated method to detect facial micro-expressions; and inventing a new way for neural networks to exploit spatiotemporal structure. He is currently exploring learning from small amount of labeled data (using techniques such as generative models, few-shot learning, transfer learning) and explainability/interpretability of deep learning models, and is particularly excited about the potential of AI in healthcare & education. His research has laid the foundation for several applications such as Face ID in iPhone X, autonomous driving, human pose estimation, detecting facial micro-expressions & translating sign language. Tomas did his PhD in deep learning with Prof Andrew Zisserman at Oxford University and bachelor’s degree in computer science at Cambridge University. He is the recipient of the Forbes 30 Under 30 award, and has received over 40 research awards, including 3 best paper awards, with numerous publications in top AI research venues. His work has been frequently featured in mainstream media, including Forbes, BusinessInsider & Wired.
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    Preview abstract Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices. View details
    Preview abstract Text-to-SQL aims to automate the process of generating SQL queries on a database from natural language text. In this work, we propose "SQLPrompt", tailored to improve the few-shot prompting capabilities of Text-to-SQL for Large Language Models (LLMs). Our methods include innovative prompt design, execution based consistency decoding strategy which selects the SQL with the most consistent execution outcome among other SQL proposals, and a method that aims to improve performance by diversifying the SQL proposals during consistency selection with different prompt designs ("MixPrompt") and foundation models ("MixLLMs"). We show that SQLPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin, closing the gap with finetuning state-of the-art with thousands of labeled data. View details
    Preview abstract For visual document understanding (VDU), self-supervised pretraining has been shown to successfully generate transferable representations, yet, effective adaptation of such representations to distribution shifts at test-time remains to be an unexplored area. We propose DocTTA, a novel test-time adaptation method for documents, that does source-free domain adaptation using unlabeled target document data. DocTTA leverages cross-modality self-supervised learning via masked visual language modeling, as well as pseudo labeling to adapt models learned on a source domain to an unlabeled target domain at test time. We introduce new benchmarks using existing public datasets for various VDU tasks, including entity recognition, key-value extraction, and document visual question answering. DocTTA shows significant improvements on these compared to the source model performance, up to 1.89% in (F1 score), 3.43% (F1 score), and 17.68% (ANLS score), respectively. View details
    Preview abstract A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-Adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot). Requiring only a small amount of unlabeled data and an inference-only LLM, USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types and then uses a corresponding selector to select the most suitable queries and zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a fully automated way. We evaluate USP with PaLM and PaLM 2 models and demonstrate performances that are considerably stronger than standard zero-shot baselines and often comparable to or even superior to few-shot baselines across more than 40 natural language understanding, natural language generation, and reasoning tasks. View details
    Preview abstract Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility. Modeling target distribution at arbitrary quantile levels and at arbitrary input attribute levels are important to offer a comprehensive picture of the data, and requires the quantile function to be expressive enough. The quantile function describing the target distribution using quantile levels is critical for quantile regression. Althought various parametric forms for the distributions (that the quantile function specifies) can be adopted, an everlasting problem is selecting the most appropriate one that can properly approximate the data distributions. In this paper, we propose a non-parametric and data-driven approach, Neural Spline Search (NSS), to represent the observed data distribution without parametric assumptions. NSS is flexible and expressive for modeling data distributions by transforming the inputs with a series of monotonic spline regressions guided by symbolic operators. We demonstrate that NSS outperforms previous methods on synthetic, real-world regression and time-series forecasting tasks. View details
    Preview abstract While remarkable progress has been made in imbalanced supervised learning, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only are few labeled data provided, but the underlying data distribution can be severely imbalanced. Recent work requires both complicated sampling strategies of pseudo-labeled unlabeled data and distribution alignment of the pseudo-label distribution to accommodate this imbalance. We present a novel approach that relies only on a form of a distribution alignment but no sampling strategy where rather than aligning the pseudo-labels during inference, we move the distribution alignment component into the respective cross entropy loss computations for both the supervised and unsupervised losses. This alignment compensates for both imbalance in the data and the eventual distributional shift present during evaluation. Altogether, this provides a unified strategy that offers both significantly reduced training requirements and improved performance across both low and richly labeled regimes and over varying degrees of imbalance. In experiments, we validate the efficacy of our method on SSL variants of CIFAR10-LT, CIFAR100-LT, and ImageNet-127. On ImageNet-127, our method shows 1.6% accuracy improvement over CReST with an 80% training time reduction and is competitive with other SOTA methods. View details
    Preview abstract Semi-supervised anomaly detection is a common problem, as often the datasets containing anomalies are partially labeled. We propose a canonical framework: Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) that isn't limited by the assumption that labeled and unlabeled data come from the same distribution. Indeed, the assumption is often violated in many applications -- for example, the labeled data may contain only anomalies unlike unlabeled data, or unlabeled data may contain different types of anomalies, or labeled data may contain only `easy-to-label' samples. SPADE utilizes an ensemble of one class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching is proposed to automatically select the critical hyper-parameters for pseudo-labeling without validation data, which is crucial with limited labeled data. SPADE shows state-of-the-art semi-supervised anomaly detection performance across a wide range of scenarios with distribution mismatch in both tabular and image domains. In some common real-world settings such as model facing new types of unlabeled anomalies, SPADE outperforms the state-of-the-art alternatives by 5% AUC in average. View details
    Preview abstract Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for small models within a multi-task training framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our 770M T5 model outperforms the 540B PaLM model using only 80% of available data on a benchmark task. View details
    Preview abstract Real-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular. However, recent work demonstrates that simple univariate linear models can outperform such deep learning models on several commonly used academic benchmarks. Extending them, in this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), a novel architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along both the time and feature dimensions to extract information efficiently. On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives. Our results underline the importance of efficiently utilizing cross-variate and auxiliary information for improving the performance of time series forecasting. We present various analyses to shed light into the capabilities of TSMixer. The design paradigms utilized in TSMixer are expected to open new horizons for deep learning-based time series forecasting. The implementation is available at: https://github.com/google-research/google-research/tree/master/ tsmixer . View details
    Preview abstract Multimodal large-scale pretraining has shown impressive performance gains for unstructured data including language, image, audio, and video. Yet, the scenario prominent in real-world applications is the existence of combination of structured (including tabular and time-series) and unstructured data in conjunction, and it has been understudied. Towards this end, we propose LANISTR, a novel attention-based framework to learn from LANguage, Image, and STRuctured data. We introduce a new multimodal fusion module with a similarity-based multimodal masking loss that enables LANISTR to learn cross-modal relations from large-scale multimodal data with missing modalities during training and test time. On two publicly available MIMIC-IV and Amazon Product Review datasets, LANISTR achieves absolute improvements of 6.47% (AUROC) and 8.35% (accuracy), respectively, compared to the state-of-the-art multimodal models, while showing superior generalization capabilities. View details
    Preview abstract The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that extend the mask language modeling to other modalities require careful multi-task tuning, complex reconstruction target designs, or additional pre-training data. In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss. The graph contrastive objective maximizes the agreement of multimodal representations, providing a natural interplay for all modalities without special customization. In addition, we extract image features within the bounding box that joins a pair of tokens connected by a graph edge, capturing more targeted visual cues without loading a sophisticated and separately pre-trained image embedder. FormNetV2 establishes new state-of-the-art performance on FUNSD, CORD, SROIE and Payment benchmarks with a more compact model size. View details
    Preview abstract Vision-language contrastive learning suggests a new learning paradigm by leveraging a large amount of image-caption-pair data. The caption supervision excels at providing wide coverage in vocabulary that enables strong zero-shot image recognition performance. On the other hand, label supervision offers to learn more targeted visual representations that are label-oriented and can cover rare categories. To gain the complementary advantages of both kinds of supervision for contrastive image-caption pre-training, recent works have proposed to convert class labels into a sentence with pre-defined templates called prompts. However, a naive unification of the real caption and the prompt sentences could lead to a complication in learning, as the distribution shift in text may not be handled properly in the language encoder. In this work, we propose a simple yet effective approach to unify these two types of supervision using prefix tokens that inform a language encoder of the type of the input sentence (e.g., caption or prompt) at training time. Our method is generic and can be easily integrated into existing VL pre-training objectives such as CLIP or UniCL. In experiments, we show that this simple technique dramatically improves the performance in zero-shot image recognition accuracy of the pre-trained model. View details
    Preview abstract Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes both the document schema and a specific entity type into a query, which is used to prompt a Transformer model to perform a single entity extraction task. Furthermore, we propose to leverage large-scale query-entity pairs generated from form-like webpages with weak HTML annotations to pre-train QueryForm. By unifying pre-training and fine-tuning into the same query-based framework, QueryForm enables models to learn from structured documents containing various entities and layouts, leading to better generalization to target document types without the need for target-specific training data. QueryForm sets new state-of-the-art average F1 score on both the XFUND (+4.6%~10.1%) and the Payment (+3.2%~9.5%) zero-shot benchmark, with a smaller model size and no additional image input. View details
    Preview abstract We study anomaly clustering, grouping data into coherent clusters of anomaly types. This is different from anomaly detection that aims to divide anomalies from normal data.Unlike object-centered image clustering, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings. The weight defines the importance of instances (i.e., patch embeddings) in the bag, which may highlight defective regions. We compute weights in an unsupervised way or in a semi-supervised way when labeled normal data is available. Extensive experimental studies show the effectiveness of the proposed clustering framework along with a novel distance function upon existing multiple instance or deep clustering frameworks. Overall, our framework achieves 0.451 and 0.674 normalized mutual information scores on MVTec object and texture categories and further improve with a few labeled normal data(0.577, 0.669), far exceeding the baselines (0.244, 0.273)or state-of-the-art deep clustering methods (0.176, 0.277). View details
    Better Zero-Shot Reasoning with Self-Adaptive Prompting
    Xingchen Wan
    Findings of the Association for Computational Linguistics (ACL) (2023)
    Preview abstract Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few-shot and zero shot abilities: they either learn from a handful of handcrafted, completed responses (“in context examples”), or are prompted to reason spontaneously through specially designed triggers. Nonetheless, few-shot performance is sensitive to the choice of the examples, for which artisanal hand-crafted selection would require extensive effort, and in some cases, it might not even be possible to obtain relevant examples a-priori without expertise about the downstream tasks. On the other hand, most general and handcrafting-free, zero-shot performance is limited by the lack of guidance to the LLM. To address this, we propose Consistency-based Self-adaptive Prompting (COSP), a novel prompt design method for LLMs. Requiring neither handcrafted responses nor ground-truth labels, COSP selects & builds the set of examples from the LLM’s own zero-shot outputs via carefully designed criteria combining consistency, diversity and repetition. In zero-shot setting, with only LLM predictions, COSP significantly improves performance (up to 2× compared to zero-shot baselines and matching or exceeding few-shot baselines) in a range of reasoning tasks in 3 LLMs. Moreover, COSP can be generalized to few-shot setting and can take advantage of few labeled examples in an efficient way View details
    Preview abstract In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad applicability of CIR. In this work, we propose to study an important task, Zero-Shot Composed Image Retrieval (ZS-CIR), whose goal is to build a CIR model without requiring labeled triplets for training. To this end, we propose a novel method, called Pic2Word, that requires only weakly labeled image-caption pairs and unlabeled image datasets to train. Unlike existing supervised CIR models, our model trained on weakly labeled or unlabeled datasets shows strong generalization across diverse ZS-CIR tasks, e.g., attribute editing, object composition, and domain conversion. Our approach outperforms several supervised CIR methods on the common CIR benchmark, CIRR and Fashion-IQ. View details
    Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
    Cheng-Yu Hsieh
    Si-An Chen
    Chun-Liang Li
    Alexander Ratner
    Ranjay Krishna
    arXiv preprint arXiv:2308.00675 (2023)
    Preview abstract Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage. Unfortunately, demonstrations are hard to acquire, and can result in undesirable biased usage if the wrong demonstration is chosen. Even in the rare scenario that demonstrations are readily available, there is no principled selection protocol to determine how many and which ones to provide. As tasks grow more complex, the selection search grows combinatorially and invariably becomes intractable. Our work provides an alternative to demonstrations: tool documentation. We advocate the use of tool documentation, descriptions for the individual tool usage, over demonstrations. We substantiate our claim through three main empirical findings on 6 tasks across both vision and language modalities. First, on existing benchmarks, zero-shot prompts with only tool documentation are sufficient for eliciting proper tool usage, achieving performance on par with few-shot prompts. Second, on a newly collected realistic tool-use dataset with hundreds of available tool APIs, we show that tool documentation is significantly more valuable than demonstrations, with zero-shot documentation significantly outperforming few-shot without documentation. Third, we highlight the benefits of tool documentations by tackling image generation and video tracking using just-released unseen state-of-the-art models as tools. Finally, we highlight the possibility of using tool documentation to automatically enable new applications: by using nothing more than the documentation of GroundingDino, Stable Diffusion, XMem, and SAM, LLMs can re-invent the functionalities of the just-released Grounded-SAM and Track Anything models. View details
    Preview abstract Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however, limits their practical value due to privacy and memory constraints. In this work, we present a simple yet effective framework, DualPrompt, which learns a tiny set of parameters, called prompts, to properly instruct a pre-trained model to learn tasks arriving sequentially without buffering past examples. DualPrompt presents a novel approach to attach complementary prompts to the pre-trained backbone, and then formulates the objective as learning task-invariant and task-specific "instructions". With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting. In particular, DualPrompt outperforms recent advanced continual learning methods with relatively large buffer sizes. We also introduce a more challenging benchmark, Split ImageNet-R, to help generalize rehearsal-free continual learning research. Source code is available at https://github.com/google-research/l2p. View details
    Decoupling Local and Global Representations of Time Series
    Sana Tonekaboni
    Chun-Liang Li
    Anna Goldenberg
    International Conference on Artificial Intelligence and Statistics (2022)
    Preview abstract Real-world time series data are often generated from several sources of variation. Learning representations that capture the factors contributing to this variability enables better understanding of the data via its underlying generative process and can lead to improvements in performance on downstream machine learning tasks. In this paper, we propose a novel generative approach for learning representations for the global and local factors of variation in time series data. The local representation of each sample models non-stationarity over time with a stochastic process prior, and the global representation of the sample encodes the time-independent characteristics. To encourage decoupling between the representations, we introduce a counterfactual regularization that minimizes the mutual information between the two variables. In experiments, we demonstrate successful recovery of the true local and global factors of variability on simulated data, and show that representations learned using our method lead to superior performance on downstream tasks on real-world datasets. We believe that the proposed way of defining representations is beneficial for data modeling and can yield better insights into the complexity of the real-world data. View details
    Preview abstract Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer. The benefits of the proposed judiciously-selected design are threefold: (1) NesT converges faster and requires much less training data to achieve good generalization on both ImageNet and small datasets like CIFAR; (2) when extending our key ideas to image generation, NesT leads to a strong decoder that is 8$\times$ faster than previous transformer-based generators; and (3) we show that decoupling the feature learning and abstraction processes via this nested hierarchy in our design enables constructing a novel method (named GradCAT) for visually interpreting the learned model. Source code is available https://github.com/google-research/nested-transformer. View details
    Preview abstract Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety of layout patterns. We propose FormNet, a structure-aware sequence model to mitigate the suboptimal serialization of forms. First, we design Rich Attention that leverages the spatial relationship between tokens in a form for more precise attention score calculation. Second, we construct Super-Tokens for each word by embedding representations from their neighboring tokens through graph convolutions. FormNet therefore explicitly recovers local syntactic information that may have been lost during serialization. In experiments, FormNet outperforms existing methods with a more compact model size and less pre-training data, establishing new state-of-the-art performance on CORD, FUNSD and Payment benchmarks. View details
    Preview abstract Learning visual knowledge from massive weakly-labeled web videos has attracted growing research interests thanks to the large corpus of easily accessible video data on the Internet. However, for video action recognition, the action of interest might only exist in arbitrary clips of untrimmed web videos, resulting in high label noises in the temporal space. To address this issue, we introduce a new method for pretraining video action recognition models using queried web videos. Instead of trying to filter out, we propose to convert the potential noises in these queried videos to useful supervision signals by defining the concept of Sub-Pseudo Label (SPL). Specifically, SPL spans out a new set of meaningful “middle ground” label space constructed by extrapolating the original weak labels during video querying and the prior knowledge distilled from a teacher model. Consequently, SPL provides enriched supervision for video models to learn better representations. SPL is fairly simple and orthogonal to popular teacher-student self-training frameworks without extra training cost. We validate the effectiveness of our method on four video action recognition datasets and a weakly-labeled image dataset to study the generalization ability. Experiments show that SPL outperforms several existing pre-training strategies using pseudolabels and the learned representations lead to competitive results when fine-tuning on HMDB-51 and UCF-101 compared with recent pre-training methods View details
    Algorithmic fairness in pandemic forecasting: lessons from COVID-19
    Thomas Tsai
    Benjamin Jacobson
    Nate Yoder
    Dario Sava
    Meg Mitchell
    Garth Graham
    npj Digital Medicine (2022)
    Preview abstract Racial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. Without careful and deliberate bias mitigation, inequities embedded in data can be transferred to model predictions, perpetuating disparities, and exacerbating the disproportionate harms of the COVID-19 pandemic. These biases in data and forecasts can be viewed through both statistical and sociological lenses, and the challenges of both building hierarchical models with limited data availability and drawing on data that reflects structural inequities must be confronted. We present an outline of key modeling domains in which unfairness may be introduced and draw on our experience building and testing the Google-Harvard COVID-19 Public Forecasting model to illustrate these challenges and offer strategies to address them. While targeted toward pandemic forecasting, these domains of potentially biased modeling and concurrent approaches to pursuing fairness present important considerations for equitable machine-learning innovation. View details
    Preview abstract Understanding black-box machine learning models is crucial for their widespread adoption. Learning globally interpretable models is one approach, but achieving high performance with them is challenging. An alternative approach is to explain individual predictions using locally interpretable models. For locally interpretable modeling, various methods have been proposed and indeed commonly used, but they suffer from low fidelity, i.e. their explanations do not approximate the predictions well. In this paper, our goal is to push the state-of-the-art in high-fidelity locally interpretable modeling. We propose a novel framework, Locally Interpretable Modeling using Instance-wise Subsampling (LIMIS). LIMIS utilizes a policy gradient to select a small number of instances and distills the black-box model into a low-capacity locally interpretable model using those selected instances. Training is guided with a reward obtained directly by measuring the fidelity of the locally interpretable models. We show on multiple tabular datasets that LIMIS near-matches the prediction accuracy of black-box models, significantly outperforming state-of-the-art locally interpretable models in terms of fidelity and prediction accuracy. View details
    Preview abstract Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for this task. In this paper, we focus on fully unsupervised AD, in which the entire training dataset, containing both normal and anomalous samples, is unlabeled. To tackle this problem effectively, we propose to improve the robustness of one-class classification trained on self-supervised representations using a data refinement process. Our proposed data refinement approach is based on an ensemble of one-class classifiers (OCCs), each of which is trained on a disjoint subset of training data. Representations learned by self-supervised learning on the refined data are iteratively updated as the data refinement improves. We demonstrate our method on various unsupervised AD tasks with image and tabular data. With a 10% anomaly ratio on CIFAR-10 image data / 2.5% anomaly ratio on Thyroid tabular data, the proposed method outperforms the state-of-the-art one-class classifier by 6.3 AUC and 12.5 average precision / 22.9 F1-score. View details
    Preview abstract The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known task identity at test time to retrieve learned knowledge and address forgetting, while this work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time. Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions. In our proposed framework, prompts are small learnable parameters, which are maintained in a memory space. The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity. We conduct comprehensive experiments under popular image classification benchmarks with different challenging continual learning settings, where L2P consistently outperforms prior state-ofthe-art methods. Surprisingly, L2P achieves competitive results against rehearsal-based methods even without a rehearsal buffer and is directly applicable to challenging taskagnostic continual learning. Source code is available at https://github.com/google-research/l2p. View details
    Preview abstract We present a two-stage framework for deep one-class classification, where in the first stage, we learn self-supervised deep representations from one-class data, and then we build a classifier using generative or discriminative models on learned representations. In particular, we present a novel distribution-augmented contrastive learning by extending training distributions via data augmentation to obstruct the uniformity of vanilla contrastive representations, yielding more suitable representations for one-class classification. Moreover, we argue that classifiers inspired by the statistical perspective in generative or discriminative ways are more effective than existing approaches, such as an average of normality scores from a surrogate classifier. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks. Not only learning a better representation, the proposed framework permits building one-class classifiers more faithful to the target task. Finally, we present visual explanations, confirming that the decision making process of our deep one-class classifier is human-intuitive. View details
    PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
    Yuliang Zou
    Han Zhang
    Chun-Liang Li
    Xiao Bian
    Jia-Bin Huang
    International Conference on Learning Representations (ICLR) (2021)
    Preview abstract Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code is available at https://github.com/googleinterns/wss. View details
    Preview abstract Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial layout patterns of documents, they have limited ability to capture reading orders of given word-level node representations in a graph. We propose Reading Order Equivariant Positional Encoding (ROPE), a new positional encoding technique designed to apprehend the sequential presentation of words in documents. ROPE generates unique reading order codes for neighboring words relative to the target word given a word-level graph connectivity. We study two fundamental document entity extraction tasks including word labeling and word grouping on the public FUNSD dataset and a large-scale payment dataset. We show that ROPE consistently improves existing GCNs with a margin up to 8.4% F1-score. View details
    Preview abstract Re-weighting training samples has been shown as an effective and practical approach to tackle data biases such as imbalance and corrupted labels. Recent methods develop learning based algorithms to learn re-weighting strategies jointly with model training, in light of reinforcement learning and meta learning. However, the dependence of additional unbiased reward data is known an undesirable limitation. Furthermore, existing learning based sample weighting methods maintain inner and outer optimization for model and weighting parameters, respectively, such that training requires expensive optimization. This paper aims to address these two problems and presents a new learning based fast sample re-weighting (FSR) method without reward data. The method is based on two key ideas, a) learning from history as dictionary fetch and b) feature sharing. Without the dependence of constructing extra reward datasets, we can easily incorporate FSR with additionally proposed task-specific components and test on label noise robust and long-tailed recognition benchmarks. Our experiments show the proposed method achieves competitive results to state-of-the-art methods in respective tasks and significantly improved training efficiency. Source code will be released. View details
    Preview abstract We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient features. We demonstrate that TabNet outperforms other neural network and decision tree variants on a wide range of non-performance-saturated tabular datasets and yields interpretable feature attributions plus insights into the global model behavior. Finally, for the first time to our knowledge, we demonstrate self-supervised learning for tabular data, significantly improving performance with unsupervised representation learning when unlabeled data is abundant. View details
    Preview abstract We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference. Deep Neural Networks with Controllable Rule Representations (DeepCTRL) incorporates a rule encoder into the model coupled with a rule-based objective, enabling a shared representation for decision making. DeepCTRL is agnostic to data type and model architecture. It can be applied to any kind of rule defined for inputs and outputs. The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio. In real-world domains where incorporating rules is critical -- such as Physics, Retail and Healthcare -- we show the effectiveness of DeepCTRL in teaching rules for deep learning. DeepCTRL improves the trust and reliability of the trained models by significantly increasing their rule verification ratio, while also providing accuracy gains at downstream tasks. Additionally, DeepCTRL enables novel use cases such as hypothesis testing of the rules on data samples, and unsupervised adaptation based on shared rules between datasets. View details
    A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan
    Arkady Epshteyn
    Ashwin Sura Ravi
    Beth Luan
    Chun-Liang Li
    Daisuke Yoneoka
    Dario Sava
    Hiroaki Miyata
    Hiroki Kayama
    Isaac Jones
    Joe Mckenna
    Johan Euphrosine
    Kris Popendorf
    Nate Yoder
    Shashank Singh
    Shuhei Nomura
    Thomas Tsai
    npj Digital Medicine (2021)
    Preview abstract The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We evaluate an AI-improved forecasting approach that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases and hospitalizations during the following 28 days. We present an international, prospective evaluation of model performance across all states and counties in the USA and prefectures in Japan. National mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths before and after prospective deployment remained consistently <3% (US) and <10% (Japan). Average statewide (US) and prefecture wide (Japan) MAPE was 6% and 20% respectively (14% when looking at prefectures with more than 10 deaths).We show our model performs well even during periods of considerable change in population behavior, and that it is robust to demographic differences across different geographic locations.We further demonstrate the model provides meaningful explanatory insights, finding that the model appropriately responds to local and national policy interventions. Our model enables counterfactual simulations, which indicate continuing NPIs alongside vaccinations is essential for more rapidly recovering from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions. View details
    Preview abstract Multi-horizon prediction problems often contain a complex mix of inputs -- including static covariates, known future inputs, and other exogenous time series -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layer for learning long-term dependencies. The TFT also utilizes specialized components for judicious selection of the relevant features, and series of gating layers to suppress unnecessary components -- enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of our model. View details
    Preview abstract In this work, we aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a simple two-stage framework for building anomaly detectors using normal training data only, where we first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at random location of a large image. Our empirical study on MVTec anomaly detection database demonstrates the proposed algorithm is general to detecting various types of real-world defects. We bring the improvement upon previous arts by 3 AUCs when learning representations from scratch. By transfer learning representations from an ImageNet pretrained model, we achieve a new state-of-the-art 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localization of defective areas without the need of annotation. View details
    Preview abstract Deep neural networks (DNNs) yield poorly-calibrated confidence estimates when their raw predicted posterior estimates are considered. Towards obtaining perfectly-calibrated confidence estimates, we propose a novel framework, named as `Distance-Based Learning from Errors' (DBLE). DBLE is based on two fundamental principles: (i) learning a representation space where distances correspond to relatedness of samples, and (ii) efficient feedback from the training errors to accurately model distances to ground truth centroids. For (i), we adapt prototypical learning such that pairwise distances determine the predicted posteriors during training, and the related samples, ideally from the same class, should be grouped together. For (ii), we propose a simple yet effective solution of relying updates on the samples that yielded the inaccurate decisions during training, with the goal of efficiently fitting a model that represents the variance of prediction in the decision manifold. On four datasets, we demonstrate that DBLE significantly outperforms alternative approaches that are based on a single DNN, in confidence calibration. DBLE is on par with ensemble approaches that contain multiple DNNs, without even doubling the training time and yielding negligible increase in the number of parameters. View details
    Preview abstract We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes. Our method, ProtoAttend, can be integrated into a wide range of neural network architectures including pre-trained models. It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes. Without sacrificing ac- curacy of the original model, ProtoAttend yields superior results in: sample-based interpretability, confidence estimation and distribution mismatch detection. View details
    Preview abstract Collecting large-scale data with clean labels for supervised training of neural networks is practically challenging. Although noisy labels are usually cheap to acquire, existing methods suffer a lot from label noise. This paper targets at the challenge of robust training at high label noise regimes. The key insight to achieve this goal is to wisely leverage a small trusted set to estimate exemplar weights and pseudo labels for noisy data in order to reuse them for supervised training. We present a holistic framework to train deep neural networks in a way that is highly invulnerable to label noise. Our method sets the new state of the art on various types of label noise and achieves leading performance on large-scale datasets with real-world label noise. For instance, on CIFAR100 with a 40% uniform noise ratio and only 10 trusted labeled data per class, our method achieves 80.2% classification accuracy, where the error rate is only 1.4% higher than a neural network trained without label noise. Moreover, increasing the noise ratio to 80%, our method still maintains a high accuracy of 75.5%, compared to the previous best accuracy 48.2%. View details
    Preview abstract Concept-based explanations can be a key direction to understand how DNNs make decisions. In this paper, we study concept-based explainability in a systematic framework. First, we define the notion of completeness, which quantifies how sufficient a particular set of concepts is in explaining the model's behavior. Based on performance and variability motivations, we propose two definitions to quantify completeness. We show that they yield the commonly-used PCA method under certain assumptions. Next, we study two additional constraints to ensure the interpretability of discovered concept, based on sparsity principles. Through systematic experiments, on specifically-designed synthetic dataset and real-world text and image datasets, we demonstrate the superiority of our framework in finding concepts that are complete (in explaining the decision) and that are interpretable. View details
    Preview abstract Quantifying the value of datum is a fundamental problem in machine learning. Besides building insights about the learning task, data valuation has applications in diverse use-cases, such as domain adaptation, corrupted sample discovery, and robust learning. To adaptively learn data values jointly with the predictive model, we propose a meta learning framework - named Data Valuator using Reinforcement Learning (DVRL). We employ a data value estimator, modeled by a deep neural network, to output how likely each datum is used in training of the predictive model. Training of the data value estimator is guided with the reinforcement signal based on a reward directly obtained from the performance on the target task. We evaluate DVRL in various applications across multiple types of datasets. DVRL yields superior quality data value estimates compared to alternative methods. The corrupted sample discovery performance of DVRL is close to optimal (i.e. as if the noisy samples are apriori known) in many regimes. For domain adaptation and robust learning tasks, outperformance of DVRL is significant - 14.6\% and 10.8\% average performance improvements, respectively. View details
    Preview abstract We propose a novel model that integrates machine learning into compartmental disease modeling to predict the progression of Covid-19. Our model incorporates explainable encoding of information-bearing covariates to improve performance. The motivation to maintain explainability is two-fold: the behavior of the resulting model will be credible with epidemiologists, and will instill confidence in the intended end-users - policy makers and healthcare institutions. The proposed model can be applied at different geographic resolutions, and we demonstrate it for United States' states and counties. We show that the forecasting accuracy of our model is significantly better than the alternatives, and the explanatory insights from it are qualitatively meaningful. View details
    Preview abstract We propose a novel framework, named learn to transfer learn (L2TL), to improve transfer learning on a target dataset by judicious extraction of information from a source dataset. Our framework considers joint optimization of vastly-shared weights between models of source and target tasks, and employs adaptive coefficients for scaling of constituent loss terms. The adaptation of the coefficients is done using a reinforcement learning (RL)-based policy model, which is guided based on a performance metric on target evaluation set. We demonstrate the state-of-the-art performance of L2TL on various datasets, with consistent outperformance of fine-tuning baselines. Especially in the regimes of small-scale target datasets and in the cases of significant label mismatch between source and target datasets, the outperformance of L2TL is more significant. View details
    Preview abstract Active learning (AL) combines data labeling and model training to minimize the labeling cost by prioritizing the selection of high value data that can best improve model performance. In pool-based active learning, accessible unlabeled data are not used for model training in most conventional methods. Here, we propose to unify unlabeled sample selection and model training towards minimizing labeling cost, and make two contributions towards that end. First, we exploit both labeled and unlabeled data using semi-supervised learning (SSL) to distill information from unlabeled data during the training stage. Second, we propose a consistency-based sample selection metric that is coherent with the training objective such that the selected samples are effective at improving model performance. We conduct extensive experiments on image classification tasks. The experimental results on CIFAR-10, CIFAR-100 and ImageNet demonstrate the superior performance of our proposed method with limited labeled data, compared to the existing methods and the alternative AL and SSL combinations. Additionally, we also study an important yet under-explored problem – “When can we start learning-based AL selection?”. We propose a measure that is empirically correlated with the AL target loss and is potentially useful for determining the proper starting point of learning-based AL methods View details
    Differentiable Top-K Operator with Optimal Transport
    Yujia Xie
    Minshuo Chen
    Tuo Zhao
    Hongyuan Zha
    Wei Wei
    NeurIPS 2020
    Preview abstract Finding the k largest or smallest elements from a collection of scores, i.e., top-k operation, is an important model component widely used in information retrieval, machine learning, and data mining. However, if the top-k operation is implemented in an algorithmic way, e.g., using bubble algorithm, the resulted model cannot be trained in an end-to-end way using prevalent gradient descent algorithms. This is because these implementations typically involve swapping indices, whose gradient cannot be computed. Moreover, the corresponding mapping from the input scores to the indicator vector of whether this element belongs to the top-k set is essentially discontinuous. To address the issue, we propose a smoothed approximation, namely SOFT (Scalable Optimal transport-based diFferenTiable) top-k operator. Specifically, our SOFT top-k operator approximates the output of top-k operation as the solution of an Entropic Optimal Transport (EOT) problem. The gradient of the SOFT operator can then be efficiently approximated based on the optimality conditions of EOT problem. We then apply the proposed operator to k-nearest neighbors algorithm and beam search algorithm. The numerical experiment demonstrates their achieve improved performance. View details
    Preview abstract The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a manifold view of the problem by introducing a smoothness constraint over the sample graph to attain harmonic functions to enforce consistent mappings during the translation. We develop HarmonicGAN to learn bi-directional translations between the source and the target domain. With the help of similarity-consistency, the inherent self-consistency property of samples can be maintained. Distance metrics defined on two types of features including histogram and CNN are exploited. Under an identical problem setting as CycleGAN without additional manual inputs, HarmonicGAN demonstrates a significant qualitative and quantitative improvement over the state of the art, as well as improved interpretability. We show experimental results in a number of applications including medical imaging, object transfiguration, and semantic labeling. We outperform the competing methods in all tasks, and for a medical imaging task in particular our method turns CycleGAN from a failure to a success, halving the mean-squared error, and generating images that radiologists prefer over competing methods in 95% of cases. View details
    Generative Modeling for Small-Data Object Detection
    Lanlan Liu
    Michael Christoph Muelly
    Jia Deng
    Jia Li
    CVPR (2019)
    Preview abstract This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. in medical images with rare diseases that doctors sometimes only see once in their life-time and where professional medical annotation resources are expensive. In this work we explore this problem from a generative modeling perspective by learning to generate new images with associated bounding boxes, and using these for training an object detector. We show that simply training an existing generative model does not yield satisfactory performance due to it optimizing for image realism instead of object detection accuracy. To this end we develop a new model that jointly learns the generative model and a detector such that the generated images improve the performance of the detector. We show that this method significantly outperforms the state of the art, improving the average precision on the NIH Chest X-ray dataset by 50% and pedestrian detection by X%. View details
    Inserting videos into videos
    Donghoon Lee
    IEEE Conference on Computer Vision and Pattern Recognition (2019)
    Preview abstract In this paper, we introduce a new problem of manipulating a given video by inserting other videos into it. Our main task is, given an object video and a scene video, inserting the object video at a user-specified location in the scene video so that the resulting video looks realistic. We aim to handle different object motions and complex backgrounds without expensive segmentation annotations. As it is difficult to collect training pairs for this problem, we synthesize fake training pairs that can provide helpful supervisory signals when training a neural network with unpaired real data. The proposed network architecture can take both real and fake pairs as input and perform both supervised and unsupervised training in adversarial learning scheme. To synthesize a realistic video, the network renders each frame based on the current input and previous frames. Under this framework, we observe that injecting noises into previous frames while generating the current frame stabilizes the training. We perform experiments on real-world videos such as object tracking or person re-identification benchmark databases. Results show that the proposed algorithm can synthesize a long sequence of a realistic video by inserting the given object video. View details
    Learning from Simulated and Unsupervised Images through Adversarial Training
    Ashish Shrivastava
    Oncel Tuzel
    Josh Susskind
    Wenda Wang
    Russ Webb
    CVPR (2017)
    Personalizing Human Video Pose Estimation
    James Charles
    Derek Magee
    David Hogg
    Andrew Zisserman
    CVPR (2016)
    Flowing ConvNets for Human Pose Estimation in Videos
    James Charles
    Andrew Zisserman
    ICCV (2015)