Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber

Neural networks often learn spurious correlations when exposed to biased training data, leading to poor performance on out-of-distribution data. A biased dataset can be divided, according to biased features, into bias-aligned samples (i.e., with biased features) and bias-conflicting samples (i.e., without biased features). Recent debiasing works typically assume that no bias label is available during the training phase, as obtaining such information is challenging and labor-intensive. Following this unsupervised assumption, existing methods usually train two models: a biased model specialized to learn biased features and a target model that uses information from the biased model for debiasing. This paper first presents experimental analyses revealing that the existing biased models overfit to bias-conflicting samples in the training data, which negatively impacts the debiasing performance of the target models. To address this issue, we propose a straightforward and effective method called \textit{Echoes}, which trains a biased model and a target model with a different strategy. We construct an "echo chamber" environment by reducing the weights of samples which are misclassified by the biased model, to ensure the biased model fully learns the biased features without overfitting to the bias-conflicting samples. The biased model then assigns lower weights on the bias-conflicting samples. Subsequently, we use the inverse of the sample weights of the biased model as the sample weights for training the target model. Experiments show that our approach achieves superior debiasing results compared to the existing baselines on both synthetic and real-world datasets.