Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin Yan Zhang Aristide Baratin Francesco Locatello Irina Rish Dianbo Liu Kenji Kawaguchi
Models prone to spurious correlations in training data often produce brittle
predictions and introduce unintended biases. Addressing this challenge
typically involves methods relying on prior knowledge and group annotation to
remove spurious correlations, which may not be readily available in many
applications. In this paper, we establish a novel connection between
unsupervised object-centric learning and mitigation of spurious correlations.
Instead of directly inferring sub-groups with varying correlations with labels,
our approach focuses on discovering concepts: discrete ideas that are shared
across input samples. Leveraging existing object-centric representation
learning, we introduce CoBalT: a concept balancing technique that effectively
mitigates spurious correlations without requiring human labeling of subgroups.
Evaluation across the Waterbirds, CelebA and ImageNet-9 benchmark datasets for
subpopulation shifts demonstrate superior or competitive performance compared
state-of-the-art baselines, without the need for group annotation.