Regularizing Attention Scores with Bootstrapping
Vision transformers (ViT) rely on attention mechanism to weigh input features, and therefore attention scores have naturally been considered as explanations for its decision-making process. However, attention scores are almost always non-zero, resulting in noisy and diffused attention maps and limiting interpretability. Can we quantify uncertainty measures of attention scores and obtain regularized attention scores? To this end, we consider attention scores of ViT in a statistical framework where independent noise would lead to insignificant yet non-zero scores. Leveraging statistical learning techniques, we introduce the bootstrapping for attention scores which generates a baseline distr...
| Authors | Neo Christopher Chung, Maxim Laletin |
|---|---|
| Primary category | cs.CV |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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