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Fine-Grained Stochastic Architecture Search
State-of-the-art deep networks are often too large to deploy on mobile d...
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MoViNets: Mobile Video Networks for Efficient Video Recognition
We present Mobile Video Networks (MoViNets), a family of computation and...
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Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Recently, differentiable search methods have made major progress in redu...
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Rethinking Bottleneck Structure for Efficient Mobile Network Design
The inverted residual block is dominating architecture design for mobile...
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Neural Architecture Search for Lightweight Non-Local Networks
Non-Local (NL) blocks have been widely studied in various vision tasks. ...
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Combined Depth Space based Architecture Search For Person Re-identification
Most works on person re-identification (ReID) take advantage of large ba...
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MemNet: Memory-Efficiency Guided Neural Architecture Search with Augment-Trim learning
Recent studies on automatic neural architectures search have demonstrate...
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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
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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