Rényi Attention Entropy for Patch Pruning
Transformers are strong baselines in both vision and language because self-attention captures long-range dependencies across tokens. However, the cost of self-attention grows quadratically with the number of tokens. Patch pruning mitigates this cost by estimating per-patch importance and removing redundant patches. To identify informative patches for pruning, we introduce a criterion based on the Shannon entropy of the attention distribution. Low-entropy patches, which receive selective and concentrated attention, are kept as important, while high-entropy patches with attention spread across many locations are treated as redundant. We also extend the criterion from Shannon to Rényi entrop...
| Authors | Hiroaki Aizawa, Yuki Igaue |
|---|---|
| Primary category | cs.CV |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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