Unsupervised Learning of Unbiased Visual Representations

TL;DR

This paper introduces an unsupervised framework for debiasing neural networks that does not require explicit bias annotations, leveraging inherent bias tendencies, pseudo-labeling, and supervised techniques to improve unbiased representation learning.

Contribution

The study proposes a novel fully unsupervised debiasing method combining bias modeling, pseudo-labeling, and supervised techniques, along with a theoretical framework for bias evaluation.

Findings

Achieves state-of-the-art debiasing performance on synthetic and real datasets.

Outperforms some fully supervised debiasing methods in certain scenarios.

Provides insights into how biases affect neural network training.

Abstract

Deep neural networks often struggle to learn robust representations in the presence of dataset biases, leading to suboptimal generalization on unbiased datasets. This limitation arises because the models heavily depend on peripheral and confounding factors, inadvertently acquired during training. Existing approaches to address this problem typically involve explicit supervision of bias attributes or reliance on prior knowledge about the biases. In this study, we address the challenging scenario where no explicit annotations of bias are available, and there's no prior knowledge about its nature. We present a fully unsupervised debiasing framework with three key steps: firstly, leveraging the inherent tendency to learn malignant biases to acquire a bias-capturing model; next, employing a pseudo-labeling process to obtain bias labels; and finally, applying cutting-edge supervised debiasing techniques to achieve an unbiased model. Additionally, we introduce a theoretical framework for evaluating model biasedness and conduct a detailed analysis of how biases impact neural network training. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness of our method, showcasing state-of-the-art performance in various settings, occasionally surpassing fully supervised debiasing approaches.