Deep Concept Removal

• URL: http://arxiv.org/abs/2310.05755v1
• Date: Mon, 9 Oct 2023 14:31:03 GMT
• Title: Deep Concept Removal
• Authors: Yegor Klochkov and Jean-Francois Ton and Ruocheng Guo and Yang Liu and Hang Li
• Abstract summary: We address the problem of concept removal in deep neural networks. We propose a novel method based on adversarial linear classifiers trained on a concept dataset. We also introduce an implicit gradient-based technique to tackle the challenges associated with adversarial training.
• Score: 29.65899467379793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of concept removal in deep neural networks, aiming to learn representations that do not encode certain specified concepts (e.g., gender etc.) We propose a novel method based on adversarial linear classifiers trained on a concept dataset, which helps to remove the targeted attribute while maintaining model performance. Our approach Deep Concept Removal incorporates adversarial probing classifiers at various layers of the network, effectively addressing concept entanglement and improving out-of-distribution generalization. We also introduce an implicit gradient-based technique to tackle the challenges associated with adversarial training using linear classifiers. We evaluate the ability to remove a concept on a set of popular distributionally robust optimization (DRO) benchmarks with spurious correlations, as well as out-of-distribution (OOD) generalization tasks.

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