2 citations · 3 across the 4 of their papers we have counts for
4 papers
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significa…
Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models
Shi-Yu Xia, Wenxuan Zhu, Xu Yang +1
In practice, we usually need to build variable-sized models adapting for diverse resource constraints in different application scenarios, where weight initialization is an importan…
Transferring Core Knowledge via Learngenes
Fu Feng, Jing Wang, Xin Geng
The pre-training paradigm fine-tunes the models trained on large-scale datasets to downstream tasks with enhanced performance. It transfers all knowledge to downstream tasks withou…
Robust Representation Learning for Unreliable Partial Label Learning
Yu Shi, Dong-Dong Wu, Xin Geng +1
Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. How…