8 citations · 19 across the 8 of their papers we have counts for
9 papers
GRU: Mitigating the Trade-off between Unlearning and Retention for LLMs
Yue Wang, Qizhou Wang, Feng Liu +4
Large language model (LLM) unlearning has demonstrated its essential role in removing privacy and copyright-related responses, crucial for their legal and safe applications. Howeve…
Adversarial Purification by Consistency-aware Latent Space Optimization on Data Manifolds
Shuhai Zhang, Jiahao Yang, Hui Luo +5
Deep neural networks (DNNs) are vulnerable to adversarial samples crafted by adding imperceptible perturbations to clean data, potentially leading to incorrect and dangerous predic…
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
Bikang Pan, Qun Li, Xiaoying Tang +6
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite…
Exclusive Style Removal for Cross Domain Novel Class Discovery
Yicheng Wang, Feng Liu, Junmin Liu +1
As a promising field in open-world learning, \textit{Novel Class Discovery} (NCD) is usually a task to cluster unseen novel classes in an unlabeled set based on the prior knowledge…
On the Learnability of Out-of-distribution Detection
Zhen Fang, Yixuan Li, Feng Liu +2
Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studi…
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
Xue Jiang, Feng Liu, Zhen Fang +4
Out-of-distribution (OOD) detection aims at identifying samples from unknown classes, playing a crucial role in trustworthy models against errors on unexpected inputs. Extensive re…
Learning to Augment Distributions for Out-of-Distribution Detection
Qizhou Wang, Zhen Fang, Yonggang Zhang +3
Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD dete…
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources
Haotian Zheng, Qizhou Wang, Zhen Fang +4
Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-wor…
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Ke Liu, Feng Liu, Haishuai Wang +3
(INRs) aim to learn a (i.e., a neural network) to represent an image, where the input and output of the fu…