基于可解释RBF分类器基础的鲁棒原型网络
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
AI总结：
针对深度原型网络（PBNs）的可解释性缺陷，提出组件分类（CBC）扩展模型，证明其鲁棒性并推导优化损失，实证显示该模型在基准上精度先进，解决可解释性问题且浅层变体优于其他浅层PBN。
摘要：
基于原型的分类学习方法本质上具有可解释性，但与深度模型相比存在性能较低等主要局限，促使了深度原型网络（PBNs，又称原型部件模型）的发展。本研究分析了这些模型的不同特性（包括可解释性），重点关注组件分类（CBC）方法——该方法使用概率模型确保可解释性，可作为浅层或深层架构。研究发现CBC存在矛盾解释等缺陷，据此提出解决这些问题的CBC扩展模型；证明该扩展具有鲁棒性保证，并推导了优化鲁棒性的损失函数。分析还表明，大多数（深度）PBN与（深度）RBF分类器相关，因此鲁棒性保证可推广到浅层RBF分类器。实证评估显示，深度扩展模型在不同基准上实现了最先进的分类精度，同时解决了其他方法的可解释性缺陷；浅层扩展变体优于其他浅层PBN，且具有固有可解释性和可证明的鲁棒性保证。
英文摘要：
Prototype-based classification learning methods are known to be inherently interpretable. However, this paradigm suffers from major limitations compared to deep models, such as lower performance. This led to the development of the so-called deep Prototype-Based Networks (PBNs), also known as prototypical parts models. In this work, we analyze these models with respect to different properties, including interpretability. In particular, we focus on the Classification-by-Components (CBC) approach, which uses a probabilistic model to ensure interpretability and can be used as a shallow or deep architecture. We show that this model has several shortcomings, like creating contradicting explanations. Based on these findings, we propose an extension of CBC that solves these issues. Moreover, we prove that this extension has robustness guarantees and derive a loss that optimizes robustness. Additionally, our analysis shows that most (deep) PBNs are related to (deep) RBF classifiers, which implies that our robustness guarantees generalize to shallow RBF classifiers. The empirical evaluation demonstrates that our deep PBN yields state-of-the-art classification accuracy on different benchmarks while resolving the interpretability shortcomings of other approaches. Further, our shallow PBN variant outperforms other shallow PBNs while being inherently interpretable and exhibiting provable robustness guarantees.
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