2 citations · 3 across the 9 of their papers we have counts for
9 papers
Rotated Robustness: A Training-Free Defense against Bit-Flip Attacks on Large Language Models
Deng Liu, Song Chen
Bit-flip corruption of quantized weights poses a serious reliability threat to Large Language Models (LLMs), as even a small number of weight faults can trigger catastrophic model…
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…
All-around Neural Collapse for Imbalanced Classification
Enhao Zhang, Chaohua Li, Chuanxing Geng +1
Neural Collapse (NC) presents an elegant geometric structure that enables individual activations (features), class means and classifier (weights) vectors to reach \textit{optimal}…
Dynamic Against Dynamic: An Open-set Self-learning Framework
Haifeng Yang, Chuanxing Geng, Pong C. Yuen +1
In open-set recognition, existing methods generally learn statically fixed decision boundaries using known classes to reject unknown classes. Though they have achieved promising re…
All Beings Are Equal in Open Set Recognition
Chaohua Li, Enhao Zhang, Chuanxing Geng +1
In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data outside given known classes as an additional +-th class to explicitly model potentia…
Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends
Xinrui Wang, Wenhai Wan, Chuanxin Geng +2
Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the i…