FairNVT：向敏感表征注入噪声实现公平分类 FairNVT: Fair Classification via Noise Injection in Vision Transformers

精读摘要

FairNVT 是一个轻量去偏框架，面向预训练 Transformer 编码器，目标是提升预测公平性的同时保住任务性能。其直觉是：减少下游分类器所用表征中的敏感属性信息，就能得到更公平的预测。具体做法是通过轻量适配器分别学习任务相关与敏感属性嵌入，向敏感嵌入注入校准过的高斯噪声，再与任务表征融合，从而在源头抑制偏见。 FairNVT is a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. The intuition is that reducing sensitive-attribute information in the representation used by the downstream classifier yields fairer predictions. It learns task-relevant and sensitive embeddings via lightweight adapters, injects calibrated Gaussian noise into the sensitive embedding, and fuses it with the task representation.