Self-supervised Debiasing Using Low Rank Regularization

Geon Yeong Park, Chanyong Jung, Sangmin Lee, Jong Chul Ye, Sang Wan Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 12395-12405

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.