5 papers
Global Pre-fixing, Local Adjusting: A Simple yet Effective Contrastive Strategy for Continual Learning
Jia Tang, Xinrui Wang, Songcan Chen
Continual learning (CL) involves acquiring and accumulating knowledge from evolving tasks while alleviating catastrophic forgetting. Recently, leveraging contrastive loss to constr…
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
Xinrui Wang, Shao-yuan Li, Jiaqiang Zhang +1
Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophi…
LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data
Chuanxing Geng, Qifei Li, Xinrui Wang +3
Using unlabeled wild data containing both in-distribution (ID) and out-of-distribution (OOD) data to improve the safety and reliability of models has recently received increasing a…
Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning
Xinrui Wang, Chuanxing Geng, Wenhai Wan +2
Recent studies have verified that semi-supervised learning (SSL) is vulnerable to data poisoning backdoor attacks. Even a tiny fraction of contaminated training data is sufficient…
Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning
Xinrui Wang, Chuanxing Geng, Wenhai Wan +2
Online continual learning requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigati…