FairViT: Fair Vision Transformer via Adaptive Masking
This addresses fairness issues in ViT models for real-world computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of fairness in Vision Transformers (ViT) by proposing FairViT, which improves accuracy without sacrificing fairness, achieving better accuracy than alternatives and appreciable fairness results.
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.