提出多层联合信息瓶颈方法，提升ViT可解释性。
Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers
- 在多层共享参数衰减率下统一计算信息瓶颈
- 显著提升归因图的忠实度，抑制冗余激活
- 适合需要深层决策解释的视觉Transformer研究
特征归因方法通过揭示输入变量对决策过程的贡献，生成解释性归因图。现有基于信息瓶颈的方法仅在特定层计算信息，通过引入参数化衰减率注入噪声压缩特征，但忽略了跨层分布的决策证据。本文提出综合信息瓶颈（CoIBA），在多个目标层中应用信息瓶颈，通过跨层共享参数衰减率来估计综合信息。该机制通过共享相关特征，弥补过度压缩导致的信息缺失，恢复被忽略的决策线索。我们采用变分方法，通过上界约束各层信息量，公平反映每层的相关性。实验表明，CoIBA确保每个目标层的丢弃激活均非决策必要，显著提升归因的忠实度。
原文摘要 · Abstract (English)
The feature attribution method reveals the contribution of input variables to the decision-making process to provide an attribution map for explanation. Existing methods grounded on the information bottleneck principle compute information in a specific layer to obtain attributions, compressing the features by injecting noise via a parametric damping ratio. However, the attribution obtained in a specific layer neglects evidence of the decision-making process distributed across layers. In this paper, we introduce a comprehensive information bottleneck (CoIBA), which discovers the relevant information in each targeted layer to explain the decision-making process. Our core idea is applying information bottleneck in multiple targeted layers to estimate the comprehensive information by sharing a parametric damping ratio across the layers. Leveraging this shared ratio complements the over-compressed information to discover the omitted clues of the decision by sharing the relevant information across the targeted layers. We suggest the variational approach to fairly reflect the relevant information of each layer by upper bounding layer-wise information. Therefore, CoIBA guarantees that the discarded activation is unnecessary in every targeted layer to make a decision. The extensive experimental results demonstrate the enhancement in faithfulness of the feature attributions provided by CoIBA.
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