COMPUTER VISION

Revisiting the Importance of Amplifying Bias for Debiasing

이정수(카이스트, 카카오엔터프라이즈), 박정훈(카이스트, 카카오엔터프라이즈), 김대영(카이스트), 이주영(카카오엔터프라이즈), 최윤재(카이스트), 주재걸(카이스트)

Association for the Advancement of Artificial Intelligence (AAAI)

2023-02-07

Abstract

In image classification, debiasing aims to train a classifier to be less susceptible to dataset bias, the strong correlation between peripheral attributes of data samples and a target class. For example, even if the frog class in the dataset mainly consists of frog images with a swamp background (i.e., biasaligned samples), a debiased classifier should be able to correctly classify a frog at a beach (i.e., bias-conflicting samples). Recent debiasing approaches commonly use two components for debiasing, a biased model f_B and a debiased model f_D. f_B is trained to focus on bias-aligned samples (i.e., overfitted to the bias) while f_D is mainly trained with bias-conflicting samples by concentrating on samples which f_B fails to learn, leading f_D to be less susceptible to the dataset bias. While the state-of-the-art debiasing techniques have aimed to better train f_D, we focus on training f_B, an overlooked component until now. Our empirical analysis reveals that removing the bias-conflicting samples from the training set for f_B is important for improving the debiasing performance of f_D. This is due to the fact that the biasconflicting samples work as noisy samples for amplifying the bias for f_B since those samples do not include the bias attribute. To this end, we propose a simple yet effective data sample selection method which removes the bias-conflicting samples to construct a bias-amplified dataset for training f_B. Our data sample selection method can be directly applied to existing reweighting-based debiasing approaches, obtaining consistent performance boost and achieving the state-of-theart performance on both synthetic and real-world datasets.