mlsanityData to 14 Oct 2026

AugShuffleNet: Communicate More, Compute Less

arXiv 2203.06589 ↗First posted 13 Mar 2022cs.CV cs.LGcited by 1

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Abstract (arXiv)

As a remarkable compact model, ShuffleNetV2 offers a good example to design efficient ConvNets but its limit is rarely noticed. In this paper, we rethink the design pattern of ShuffleNetV2 and find that the channel-wise redundancy problem still constrains the efficiency improvement of Shuffle block in the wider ShuffleNetV2. To resolve this issue, we propose another augmented variant of shuffle block in the form of bottleneck-like structure and more implicit short connections. To verify the effectiveness of this building block, we further build a more powerful and efficient model family, termed as AugShuffleNets. Evaluated on the CIFAR-10 and CIFAR-100 datasets, AugShuffleNet consistently outperforms ShuffleNetV2 in terms of accuracy with less computational cost and fewer parameter count.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 25 references that are in the map. First posted on arXiv 13 Mar 2022.

Builds on

21 of 25 references.

References · 21

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9 more
  1. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  2. 2016Deep Networks with Stochastic Depthcited by 979
  3. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023On Calibration of Modern Quantized Efficient Neural Networkscited by 1