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Status: Unchecked ×Keyword: texture bias ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs

   Ding, Zhang, Zhou, Han, Ding and Sun · arXiv (Cornell University) · 2022

   The paper proposes RepLKNet, a pure CNN with kernels up to 31x31, built on five design guidelines, and reports that it competes with vision transformers on image tasks.

   Unchecked3 claims
   Show 3 claims

   1. UncheckedThe paper says RepLKNet, a CNN with very large kernels, narrows the gap with vision transformers, matching or beating Swin Transformer with lower latency.“RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency.”
   2. UncheckedRepLKNet, a large-kernel CNN, is reported to scale to big data and large models, reaching 87.8% top-1 on ImageNet and 56.0% mIoU on ADE20K.“RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes.”
   3. UncheckedLarge-kernel CNNs are reported to have much larger effective receptive fields and rely more on object shape than on texture, compared with small-kernel CNNs.“Our study further reveals that, in contrast to small-kernel CNNs, large-kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias.”