The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
Open accessAbstract
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 10^14x 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 421x 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.
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Cites
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Cited by
ResNeSt: Split-Attention Networks
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Review of Image Classification Algorithms Based on Convolutional Neural Networks
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HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
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On-device machine learning An algorithms and learning theory perspective
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AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
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Learning Versatile Convolution Filters for Efficient Visual Recognition
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Metadata
- Authors
- Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., Wu, B., Yu, M., Xu, T., Chen, K., Vajda, P., & Gonzalez, J. E.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12962-12971
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034609471
- Cited by
- 305
- References
- 15
- FWCI
- 17.19
- Type
- conference paper
- Language
- en
- Open access
- green
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