BriefGPT.xyz
Jun, 2024
事后原型网络
Post-hoc Part-prototype Networks
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Andong Tan, Fengtao Zhou, Hao Chen
TL;DR
通过将训练模型的分类头部解构为一组可解释性零部件原型,我们提出了第一个用于事后解释的零部件原型网络,通过无监督原型发现和精化策略,确保模型的性能并提供更准确的解释和更好的零部件原型。
Abstract
post-hoc explainability
methods such as
grad-cam
are popular because they do not influence the performance of a trained model. However, they mainly reveal "where" a model looks at for a given input, fail to expla
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