Papers › Revisiting the Importance of Amplifying Bias for Debiasing
Revisiting the Importance of Amplifying Bias for Debiasing
Jungsoo Lee, Jeonghoon Park, Daeyoung Kim, Juyoung Lee, Edward Choi, Jaegul Choo
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., bias-aligned 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 bias-conflicting 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-the-art performance on both synthetic and real-world datasets.
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