2 citations · 2 across the 2 of their papers we have counts for
6 papers
Split Adaptation for Pre-trained Vision Transformers
Lixu Wang, Bingqi Shang, Yi Li +4
Vision Transformers (ViTs), extensively pre-trained on large-scale datasets, have become essential to foundation models, allowing excellent performance on diverse downstream tasks…
On Large Language Model Continual Unlearning
Chongyang Gao, Lixu Wang, Kaize Ding +3
While large language models have demonstrated impressive performance across various domains and tasks, their security issues have become increasingly severe. Machine unlearning has…
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Lixu Wang, Xinyu Du, Qi Zhu
Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the…
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Lixu Wang, Yang Zhao, Jiahua Dong +5
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid…
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Payal Mohapatra, Lixu Wang, Qi Zhu
Pattern recognition is a fundamental task in continuous sensing applications, but real-world scenarios often experience distribution shifts that necessitate learning generalizable…
Federated Continual Novel Class Learning
Lixu Wang, Chenxi Liu, Junfeng Guo +4
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique. However, most existing FL studies assume that the data distribution remains…