Deep Concept Removal
Yegor Klochkov Jean-Francois Ton Ruocheng Guo Yang Liu Hang Li
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.