RESEARCH

COMPUTER VISION

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions

June 1, 2020

Abstract

Differentiable Neural Architecture Search (DNAS) has demonstrated great success in designing state-of-the-art, efficient neural networks. However, DARTS-based DNAS’s search space is small when compared to other search methods’, since all candidate network layers must be explicitly instantiated in memory. To address this bottleneck, we propose a memory and computationally efficient DNAS variant: DMaskingNAS. This algorithm expands the search space by up to 1014 × over conventional DNAS, supporting searches over spatial and channel dimensions that are otherwise prohibitively expensive: input resolution and number of filters. We propose a masking mechanism for feature map reuse, so that memory and computational costs stay nearly constant as the search space expands. Furthermore, we employ effective shape propagation to maximize per-FLOP or per-parameter accuracy. The searched FBNetV2s yield state-of-the-art performance when compared with all previous architectures. With up to 421× less search cost, DMaskingNAS finds models with 0.9% higher accuracy, 15% fewer FLOPs than MobileNetV3-Small; and with similar accuracy but 20% fewer FLOPs than Efficient-B0. Furthermore, our FBNetV2 outperforms MobileNetV3 by 2.6% in accuracy, with equivalent model size. FBNetV2 models are open-sourced at https://github.com/facebookresearch/mobile-vision.

Download the Paper

AUTHORS

Written by

Alvin Wan

Xiaoliang Dai

Peizhao Zhang

Zijian He

Yuandong Tian

Saining Xie

Bichen Wu

Matthew Yu

Tao Xu

Kan Chen

Peter Vajda

Joseph E. Gonzalez

Publisher

Conference on Computer Vision and Pattern Recognition (CVPR)

Research Topics

Computer Vision

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