Fine-Grained Stochastic Architecture Search

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.

• Machine Intelligence

• Machine Perception