FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
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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 10^14× 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 accuracy but 20 outperforms MobileNetV3 by 2.6 FBNetV2 models are open-sourced at https://github.com/facebookresearch/mobile-vision.
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