Saurabh Singh
I am a researcher working in the field of Computer Vision and Machine Learning. My current work focuses on lossy multimedia compression using Deep Neural Networks.
I was a PhD candidate advised by David Forsyth and Derek Hoiem in the Computer Science Department at University of Illinois, Urbana-Champaign. I interned at Google Research in 2013. I received my MS from Robotics Institute, Carnegie Mellon University in 2011 where I worked with Alyosha Efros, Abhinav Gupta and Martial Hebert. My earlier research focused on discovery and use of context for various tasks including classification, pose estimation, localizing landmarks with little local appearance and visual question answering.
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Nonlinear Transform Coding
Philip A. Chou
Sung Jin Hwang
IEEE Trans. on Special Topics in Signal Processing, 15 (2021) (to appear)
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We review a class of methods that can be collected under the name nonlinear transform coding (NTC), which over the past few years have become competitive with the best linear transform codecs for images, and have superseded them in terms of rate–distortion performance under established perceptual quality metrics such as MS-SSIM. We assess the empirical rate–distortion performance of NTC with the help of simple example sources, for which the optimal performance of a vector quantizer is easier to estimate than with natural data sources. To this end, we introduce a novel variant of entropy-constrained vector quantization. We provide an analysis of various forms of stochastic optimization techniques for NTC models; review architectures of transforms based on artificial neural networks, as well as learned entropy models; and provide a direct comparison of a number of methods to parameterize the rate–distortion trade-off of nonlinear transforms, introducing a simplified one.
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Deep Implicit Volume Compression
Danhang "Danny" Tang
Phil Chou
Christian Haene
Mingsong Dou
Jonathan Taylor
Shahram Izadi
Sofien Bouaziz
Cem Keskin
CVPR (2020)
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We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in voxel grids and their corresponding textures. To compress the TSDF our method relies on a block-based neural architecture trained end-to-end achieving state-of-the-art compression rates. To prevent topological errors we losslessly compress the signs of the TSDF which also as a side effect bounds the maximum reconstruction error by the voxel size. To compress the affiliated texture we designed a fast block-base charting and Morton packing technique generating a coherent image that can be efficiently compressed using existing image-based compression algorithms. We demonstrate the performance of our algorithms on a large set of 4D performance sequences captured using multi-camera RGBD setups.
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End-to-end Learning of Compressible Features
Abhinav Shrivastava
2020 IEEE Int. Conf. on Image Processing (ICIP)
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Pre-trained convolutional neural networks (CNNs) are very powerful as an off the shelf feature generator and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression while general purpose lossy alternatives (e.g. dimensionality reduction techniques) are sub-optimal as they end up losing important information. We propose a learned method that jointly optimizes for compressibility along with the original objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that features learned by our method maintain their informativeness while being order of magnitude more compressible.
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Scalable Model Compression by Entropy Penalized Reparameterization
Deniz Oktay
Abhinav Shrivastava
8th Int. Conf. on Learning Representations (ICLR) (2020)
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We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a “latent” space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter representation during training, and to compress the representation using a simple arithmetic coder after training. Classification accuracy and model compressibility is maximized jointly, with the bitrate–accuracy trade-off specified by a hyperparameter. We evaluate the method on the MNIST, CIFAR-10 and ImageNet classification benchmarks using six distinct model architectures. Our results show that state-of-the-art model compression can be achieved in a scalable and general way without requiring complex procedures such as multi-stage training.
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Batch normalization (BN) has been very effective for deep learning and is widely used. However, when training with small minibatches, models using BN exhibit a significant degradation in performance. In this paper we study this peculiar behavior of BN to gain a better understanding of the problem, and identify a cause. We propose 'EvalNorm' to address the issue by estimating corrected normalization statistics to use for BN during evaluation. EvalNorm supports online estimation of the corrected statistics while the model is being trained, and does not affect the training scheme of the model. As a result, EvalNorm can also be used with existing pre-trained models allowing them to benefit from our method. EvalNorm yields large gains for models trained with smaller batches. Our experiments show that EvalNorm performs 6.18% (absolute) better than vanilla BN for a batchsize of 2 on ImageNet validation set and from 1.5 to 7.0 points (absolute) gain on the COCO object detection benchmark across a variety of setups.
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Image-dependent local entropy models for image compression with deep networks
Sung Jin Hwang
Michele Covell
International Conference on Image Processing (2018)
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The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that is directly optimized for rate-distortion performance. We show that this approach can be significantly improved by incorporating spatially local, image-dependent entropy models. The key insight is that existing ANN-based methods learn an entropy model that is shared between the encoder and decoder, but they do not transmit any side information that would allow the model to adapt to the structure of a specific image. We present a method for augmenting ANN-based image coders with image-dependent side information that leads to a 17.8% rate reduction over a state-of-the-art ANN-based baseline model on a standard evaluation set, and 70-98% reductions on images with low visual complexity that are poorly captured by a fixed, global entropy model.
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Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates in Recurrent Convolutional Neural Networks
Damien Vincent
Michele Covell
Sung Jin Hwang
Joel Shor
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
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We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network’s hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks.
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Variational Image Compression with a Scale Hyperprior
Sung Jin Hwang
6th Int. Conf. on Learning Representations (ICLR) (2018)
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We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies in the latent representation. This hyperprior relates to side information, a concept universal to virtually all modern image codecs, but largely unexplored in image compression using artificial neural networks (ANNs). Unlike existing autoencoder compression methods, our model trains a complex prior jointly with the underlying autoencoder. We demonstrate that this model leads to state-of-the-art image compression when measuring visual quality using the popular MS-SSIM index, and yields rate–distortion performance surpassing published ANN-based methods when evaluated using a more traditional metric based on squared error (PSNR). Furthermore, we provide a qualitative comparison of models trained for different distortion metrics.
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No Fuss Distance Metric Learning using Proxies
Alexander Toshev
Sergey Ioffe
International Conference on Computer Vision (ICCV), IEEE (2017)
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We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point x is similar to a set of positive points Y, and dissimilar to a set of negative points Z, and a loss defined over these distances is minimized.
While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.
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The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10x or 100x? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between enormous data and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.
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