Google Research

Fine-Grained Stochastic Architecture Search

ICLR Workshop on Neural Architecture Search, @article{chaudhuri2020fine, title={Fine-grained stochastic architecture search}, author={Chaudhuri, Shraman Ray and Eban, Elad and Li, Hanhan and Moroz, Max and Movshovitz-Attias, Yair}, journal={ICLR Workshop on Neural Architecture Search}, year={2020} } (2020)

Abstract

State-of-the-art deep networks are often too large to deploy on mobile devices and embedded systems. Mobile neural architecture search (NAS) methods automate the design of small models but state-of-the-art NAS methods are expensive to run. Differentiable neural architecture search (DNAS) methods reduce the search cost but explore a limited subspace of candidate architectures. In this paper, we introduce Fine-Grained Stochastic Architecture Search (FiGS), a differentiable search method that searches over a much larger set of candidate architectures. FiGS simultaneously selects and modifies operators in the search space by applying a structured sparse regularization penalty based on the Logistic-Sigmoid distribution. We show results across 3 existing search spaces, matching or outperforming the original search algorithms and producing state-of-the-art parameter-efficient models on ImageNet (e.g., 75.4% top-1 with 2.6M params). Using our architectures as backbones for object detection with SSDLite, we achieve significantly higher mAP on COCO (e.g., 25.8 with 3.0M params) than MobileNetV3 and MnasNet.

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