Abstract
Image classifiers often rely overly on peripheral attributes that have a strong correlation with the target class (i.e., dataset bias) when making predictions. Recently, a myriad of studies focus on mitigating such dataset bias, the task of which is referred to as debiasing. However, these debiasing methods often have inconsistent experimental settings (e.g., datasets and neural network architectures). Additionally, most of the previous studies in debiasing do not specify how they select their model parameters which involve early stopping and hyper-parameter tuning. The goal of this paper is to standardize the inconsistent experimental settings and propose a consistent model parameter selection criterion for debiasing. Based on such unified experimental settings and model parameter selection criterion, we build a benchmark named DebiasBench which includes five datasets and seven debiasing methods. We carefully conduct extensive experiments in various aspects and show that different state-of-the-art methods work best in different datasets, respectively. Even, the vanilla method, the method with no debiasing module, also shows competitive results in datasets with low bias severity. We publicly release the implementation of existing debiasing methods in DebiasBench to encourage future researchers in debiasing to conduct fair comparisons and further push the state-of-the-art performances.
Abstract (translated)
URL
https://arxiv.org/abs/2206.03680
