BiasEnsemble: Revisiting the Importance of Amplifying Bias for Debiasing

• URL: http://arxiv.org/abs/2205.14594v1
• Date: Sun, 29 May 2022 07:55:06 GMT
• Title: BiasEnsemble: Revisiting the Importance of Amplifying Bias for Debiasing
• Authors: Jungsoo Lee, Jeonghoon Park, Daeyoung Kim, Juyoung Lee, Edward Choi, Jaegul Choo
• Abstract summary: "Debiasing" aims to train a classifier to be less susceptible to dataset bias. $f_B$ is trained to focus on bias-aligned samples while $f_D$ is mainly trained with bias-conflicting samples. We propose a novel biased sample selection method BiasEnsemble which removes the bias-conflicting samples.
• Score: 31.665352191081357
• License: http://creativecommons.org/licenses/by/4.0/
• 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., 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 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 $f_B$. To this end, we propose a novel biased sample selection method BiasEnsemble which removes the bias-conflicting samples via leveraging additional biased models to construct a bias-amplified dataset for training $f_B$. Our simple yet effective approach 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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