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Spectral-Normalized Identity Priors
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Spectral-Normalized Identity Priors, or SNIP, is a structured pruning approach that penalizes an entire residual module in a Transformer model toward an identity mapping. It is applicable to any structured module, including a single attention head, an entire attention block, or a feed-forward subnetwork. The method identifies and discards unimportant non-linear mappings in the residual connections by applying a thresholding operator on the function norm. Furthermore, spectral normalization to stabilize the distribution of the post-activation values of the Transformer layers, further improving the pruning effectiveness of the proposed methodology.
Papers archive 2025-07-28
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Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior 5 Oct 2020 · 1 repository · arXiv:2010.01791
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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