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arXiv · 2204.00492

Provable concept learning for interpretable predictions using variational autoencoders

Armeen Taeb·Nicolo Ruggeri·Carina Schnuck·Fanny Yang
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Abstract

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.

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Armeen Taeb, Nicolo Ruggeri, Carina Schnuck, Fanny Yang. 2022-04-01. Provable concept learning for interpretable predictions using variational autoencoders. https://arxiv.org/abs/2204.00492

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