  Improperly constructed datasets can result in inaccurate inferences. For
instance, models trained on biased datasets perform poorly in terms of
generalization (i.e., dataset bias). Recent debiasing techniques have
successfully achieved generalization performance by underestimating
easy-to-learn samples (i.e., bias-aligned samples) and highlighting
difficult-to-learn samples (i.e., bias-conflicting samples). However, these
techniques may fail owing to noisy labels, because the trained model recognizes
noisy labels as difficult-to-learn and thus highlights them. In this study, we
find that earlier approaches that used the provided labels to quantify
difficulty could be affected by the small proportion of noisy labels.
Furthermore, we find that running denoising algorithms before debiasing is
ineffective because denoising algorithms reduce the impact of
difficult-to-learn samples, including valuable bias-conflicting samples.
Therefore, we propose an approach called denoising after entropy-based
debiasing, i.e., DENEB, which has three main stages. (1) The prejudice model is
trained by emphasizing (bias-aligned, clean) samples, which are selected using
a Gaussian Mixture Model. (2) Using the per-sample entropy from the output of
the prejudice model, the sampling probability of each sample that is
proportional to the entropy is computed. (3) The final model is trained using
existing denoising algorithms with the mini-batches constructed by following
the computed sampling probability. Compared to existing debiasing and denoising
algorithms, our method achieves better debiasing performance on multiple
benchmarks.
