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Status: Unchecked ×Keyword: feature space clustering ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Global Channel Pruning With Self-Supervised Mask Learning

   Ma, Zhang, Wang, Wang, Du and Li · IEEE Transactions on Circuits and Systems for Video Technology · 2024

   The paper proposes SSML, a self-supervised way to learn which network channels to prune globally, and reports that it outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet.

   Unchecked2 claims
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   1. UncheckedThe paper's proposed loss groups similar channel masks and separates dissimilar ones, so channels with mask 0 can be removed without hurting the pruned model.“The proposed loss function distinguishes the similarity between pseudo-labels and masks, clustering similar masks together in the feature space and separating dissimilar masks, ultimately allowing channels with masks of 0 to be safely removed without damaging the performance of the pruned model.”
   2. UncheckedThe paper says its pruning method, SSML, can be trained from scratch to produce a compact (smaller) neural network.“In addition, SSML can train from scratch to yield a compact model.”