论文标题
Glancenets：解释性，防泄漏概念的模型
GlanceNets: Interpretabile, Leak-proof Concept-based Models
论文作者
论文摘要
人们对基于概念的模型（CBM）的兴趣越来越多，这些模型（CBM）通过获取和推理与高级概念的词汇结合了高性能和解释性。一个关键要求是这些概念是可解释的。现有的CBM使用基于不清楚的解释性概念来解决这一Desidatum，并且无法用预期的语义获得概念。我们通过根据模型的表示与潜在的数据生成过程的对齐方式提供清晰的可解释性定义来解决这一问题，并引入Glancenets，Glancenets是一种新的CBM，一种新的CBM利用技术来利用技术的表示和开放设定的识别来实现一致性，从而提高了对学习概念的解释性。我们表明，与概念级的监督相比，Glancenets比最先进的方法更好地保持一致性，同时又可以防止虚假信息无意间泄漏到学习的概念中。
There is growing interest in concept-based models (CBMs) that combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to acquire concepts with the intended semantics. We address this by providing a clear definition of interpretability in terms of alignment between the model's representation and an underlying data generation process, and introduce GlanceNets, a new CBM that exploits techniques from disentangled representation learning and open-set recognition to achieve alignment, thus improving the interpretability of the learned concepts. We show that GlanceNets, paired with concept-level supervision, achieve better alignment than state-of-the-art approaches while preventing spurious information from unintendedly leaking into the learned concepts.
