论文标题
使用变分自动编码器的可解释预测的可解释预测的概念学习
Provable concept learning for interpretable predictions using variational autoencoders
论文作者
论文摘要
在关键安全应用中，当没有可解释的解释时，从业人员不愿信任神经网络。许多尝试提供此类解释的尝试围绕基于像素的属性或使用先前已知的概念。在本文中，我们旨在通过证明\ emph {高级，以前未知的地面概念}来提供解释。为此，我们提出了一个概率建模框架来得出（c）插入（l）收入和（p）rediction（clap） - 基于VAE的分类器，该分类器使用可视上可解释的概念作为简单分类器的预测指标。假设采用了地面概念的生成模型，我们证明拍手能够在达到最佳分类精度的同时识别它们。我们对合成数据集的实验验证了拍手确定合成数据集的不同基础概念，并在医疗胸部X射线数据集上产生有希望的结果。
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel-based attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying \emph{high-level, previously unknown ground-truth concepts}. To this end, we propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) -- a VAE-based classifier that uses visually interpretable concepts as predictors for a simple classifier. Assuming a generative model for the ground-truth concepts, we prove that CLAP is able to identify them while attaining optimal classification accuracy. Our experiments on synthetic datasets verify that CLAP identifies distinct ground-truth concepts on synthetic datasets and yields promising results on the medical Chest X-Ray dataset.
