STEEX: Steering Counterfactual Explanations with Semantics

• URL: http://arxiv.org/abs/2111.09094v1
• Date: Wed, 17 Nov 2021 13:20:29 GMT
• Title: STEEX: Steering Counterfactual Explanations with Semantics
• Authors: Paul Jacob, \'Eloi Zablocki, H\'edi Ben-Younes, Micka\"el Chen, Patrick P\'erez, Matthieu Cord
• Abstract summary: Deep learning models are increasingly used in safety-critical applications. For simple images, such as low-resolution face portraits, visual counterfactual explanations has recently been proposed. We propose a new generative counterfactual explanation framework that produces plausible and sparse modifications.
• Score: 28.771471624014065
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a trained classification model. In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausible and sparse modifications which preserve the overall scene structure. Furthermore, we introduce the concept of "region-targeted counterfactual explanations", and a corresponding framework, where users can guide the generation of counterfactuals by specifying a set of semantic regions of the query image the explanation must be about. Extensive experiments are conducted on challenging datasets including high-quality portraits (CelebAMask-HQ) and driving scenes (BDD100k).

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