FairSTG: Countering performance heterogeneity via collaborative sample-level optimization

no code implementations • 19 Mar 2024 • Gengyu Lin, Zhengyang Zhou, Qihe Huang, Kuo Yang, Shifen Cheng, Yang Wang

To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up.

Fairness Graph Learning +1