FairViT: Fair Vision Transformer via Adaptive Masking

TL;DR

FairViT is a novel framework for Vision Transformers that enhances fairness without sacrificing accuracy by using adaptive masking and a new distance loss, achieving superior performance and fairness in vision tasks.

Contribution

The paper introduces FairViT, a new fair and accurate Vision Transformer framework utilizing adaptive masks and a distance loss to improve fairness and accuracy simultaneously.

Findings

FairViT outperforms existing methods in accuracy.

FairViT achieves significant fairness improvements.

The approach maintains computational efficiency.

Abstract

Vision Transformer (ViT) has achieved excellent performance and demonstrated its promising potential in various computer vision tasks. The wide deployment of ViT in real-world tasks requires a thorough understanding of the societal impact of the model. However, most ViT-based works do not take fairness into account and it is unclear whether directly applying CNN-oriented debiased algorithm to ViT is feasible. Moreover, previous works typically sacrifice accuracy for fairness. Therefore, we aim to develop an algorithm that improves accuracy without sacrificing fairness. In this paper, we propose FairViT, a novel accurate and fair ViT framework. To this end, we introduce a novel distance loss and deploy adaptive fairness-aware masks on attention layers updating with model parameters. Experimental results show \sys can achieve accuracy better than other alternatives, even with competitive computational efficiency. Furthermore, \sys achieves appreciable fairness results.