Self-supervised debiasing using low rank regularization
This addresses the challenge of training debiased models with limited annotations, which is crucial for improving robustness in real-world applications where labeled data is scarce.
The paper tackles the problem of debiasing deep neural networks from spurious correlations without requiring full supervision, by proposing a self-supervised framework that uses rank regularization to identify and upweight bias-conflicting samples, resulting in significant improvements in generalization performance, sometimes outperforming supervised state-of-the-art methods.
Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. While most existing debiasing methods require full supervision on either spurious attributes or target labels, training a debiased model from a limited amount of both annotations is still an open question. To address this issue, we investigate an interesting phenomenon using the spectral analysis of latent representations: spuriously correlated attributes make neural networks inductively biased towards encoding lower effective rank representations. We also show that a rank regularization can amplify this bias in a way that encourages highly correlated features. Leveraging these findings, we propose a self-supervised debiasing framework potentially compatible with unlabeled samples. Specifically, we first pretrain a biased encoder in a self-supervised manner with the rank regularization, serving as a semantic bottleneck to enforce the encoder to learn the spuriously correlated attributes. This biased encoder is then used to discover and upweight bias-conflicting samples in a downstream task, serving as a boosting to effectively debias the main model. Remarkably, the proposed debiasing framework significantly improves the generalization performance of self-supervised learning baselines and, in some cases, even outperforms state-of-the-art supervised debiasing approaches.