3 accepted papers
Continual learning (CL) with large pre-trained models is challenged by task interference and catastrophic forgetting. Existing LoRA-based Mixture-of-Experts (MoE) methods mitigate forgetting by adding new task-specific adapters and freezing old ones, but often suffer from redundancy, interference, a…
In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims,
Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learning models in the real world. Distance-based OOD detection methods have emerged with enhanced deep representation learning…