5 papers
Rotated Robustness: A Training-Free Defense against Bit-Flip Attacks on Large Language Models
Deng Liu, Song Chen
Hardware faults, specifically bit-flips in quantized weights, pose a severe reliability threat to Large Language Models (LLMs), often triggering catastrophic model collapses. We de…
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…
ULFine: Unbiased Lightweight Fine-tuning for Foundation-Model-Assisted Long-Tailed Semi-Supervised Learning
Enhao Zhang, Chaohua Li, Chuanxing Geng +1
Based on the success of large-scale visual foundation models like CLIP in various downstream tasks, this paper initially attempts to explore their impact on Long-Tailed Semi-Superv…
Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey
Chaohua Li, Enhao Zhang, Chuanxing Geng +1
Out-of-distribution detection (OOD) is a pivotal task for real-world applications that trains models to identify samples that are distributionally different from the in-distributio…
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…