2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Federated Learning for Inference at Anytime and Anywhere
Zicheng Liu, Da Li, Javier Fernandez-Marques +6
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication…
cs.CV2022
Learning to Augment via Implicit Differentiation for Domain Generalization
Tingwei Wang, Da Li, Kaiyang Zhou +2
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) a…
cs.CV2022★ 2 cited
Robust Target Training for Multi-Source Domain Adaptation
Zhongying Deng, Da Li, Yi-Zhe Song +1
Given multiple labeled source domains and a single target domain, most existing multi-source domain adaptation (MSDA) models are trained on data from all domains jointly in one ste…