A2: Adaptive Augmentation for Effectively Mitigating Dataset Bias

Jaeju An, Taejune Kim, Donggeun Ko, Sangyup Lee, Simon S. Woo

Abstract: Recently, deep neural networks (DNNs) have become the de facto standard to achieve outstanding performances and demonstrate significant impact on various computer vision tasks for real-world scenarios. However, the trained networks can often suffer from overfitting issues due to the unintended bias in a dataset causing inaccurate, unreliable, and untrustworthy results. Thus, recent studies have attempted to remove bias by augmenting the bias-conflict samples to address this challenge. Yet, it still remains a challenge since generating bias-conflict samples without human supervision is generally difficult. To tackle this problem, we propose a novel augmentation framework, Adaptive Augmentation (A $$^{2}$$ ), based on a generative model that help classifiers learn debiased representations. Our framework consists of three steps: 1) extracting bias-conflict samples from a biased dataset in an unsupervised manner, 2) training a generative model with the biased dataset and adapting the learned biased distribution to the extracted bias-conflict samples’ distribution, and 3) augmenting bias-conflict samples by translating bias-align samples. Therefore, our classifier can effectively learn the debiased representation without human supervision. Our extensive experimental results demonstrate that A $$^{2}$$  effectively augments bias-conflict samples, mitigating widespread bias issues. The code is available in here ( https://github.com/anjaeju/A2-Adaptive-Augmentation-for-Effectively-Mitigating-Dataset-Bias ).