BriefGPT.xyz
Jan, 2021
通过多元对手应对训练中的偏差问题
Diverse Adversaries for Mitigating Bias in Training
HTML
PDF
Xudong Han, Timothy Baldwin, Trevor Cohn
TL;DR
本文提出了一种基于多个不同鉴别器的新型对抗学习方法,通过鼓励鉴别器相互学习正交隐藏表征,从而显著改善了标准对抗去偏差方法对于降低偏见和提升训练稳定性的效果。
Abstract
adversarial learning
can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model
bias
, added to which their training procedures
→