让神经网络的决策解释更简洁清晰，只保留关键特征。
SIDE: Sparse Information Disentanglement for Explainable Artificial Intelligence
- 通过稀疏训练与剪枝，仅保留少数关键原型。
- 解释规模缩小90%以上，准确率不降。
- 适合医疗影像等需要透明决策的场景。
理解深度神经网络在医疗影像、自动驾驶等高风险领域中的决策至关重要，但现有模型常缺乏透明性。基于原型的神经网络可提供概念级解释，但多数仅适用于细粒度分类任务。尽管InfoDisent已扩展至ImageNet等大规模数据集，其解释仍过于复杂。本文提出稀疏信息解耦方法（SIDE），通过专用训练与剪枝策略强制稀疏性，并采用Sigmoid激活替代Softmax，使每类仅关联少量相关原型。大量实验表明，SIDE在保持现有方法准确率的同时，将解释规模减少超过90%，显著提升原型解释的可理解性。
原文摘要 · Abstract (English)
Understanding the decisions made by deep neural networks is essential in high-stakes domains such as medical imaging and autonomous driving. Yet, these models often lack transparency, particularly in computer vision. Prototypical-parts-based neural networks have emerged as a promising solution by offering concept-level explanations. However, most are limited to fine-grained classification tasks, with few exceptions such as InfoDisent. InfoDisent extends prototypical models to large-scale datasets like ImageNet, but produces complex explanations. We introduce Sparse Information Disentanglement for Explainability (SIDE), a novel method that improves the interpretability of prototypical parts through a dedicated training and pruning scheme that enforces sparsity. Combined with sigmoid activations in place of softmax, this approach allows SIDE to associate each class with only a small set of relevant prototypes. Extensive experiments show that SIDE matches the accuracy of existing methods while reducing explanation size by over $90\%$, substantially enhancing the understandability of prototype-based explanations.
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