  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
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 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. Our code is available at
https://github.com/isruihu/Echoes.

Machine Learning;Artificial Intelligence