149 citations · 212 across the 10 of their papers we have counts for
11 papers
Robust Training of Federated Models with Extremely Label Deficiency
Yonggang Zhang, Zhiqin Yang, Xinmei Tian +3
Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Adva…
Gradient constrained sharpness-aware prompt learning for vision-language models
Liangchen Liu, Nannan Wang, Dawei Zhou +4
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i.e., improving the performance on unseen classes while maintaining…
Strength-Adaptive Adversarial Training
Chaojian Yu, Dawei Zhou, Li Shen +5
Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations…
Improving Adversarial Robustness via Mutual Information Estimation
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative…
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
De Cheng, Tongliang Liu, Yixiong Ning +5
In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifie…
Modeling Adversarial Noise for Adversarial Training
Dawei Zhou, Nannan Wang, Bo Han +1
Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adve…
Kernel Mean Estimation by Marginalized Corrupted Distributions
Xiaobo Xia, Shuo Shan, Mingming Gong +4
Estimating the kernel mean in a reproducing kernel Hilbert space is a critical component in many kernel learning algorithms. Given a finite sample, the standard estimate of the tar…
Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han +4
The label noise transition matrix , reflecting the probabilities that true labels flip into noisy ones, is of vital importance to model label noise and design statistically cons…
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia, Tongliang Liu, Bo Han +6
Learning with the \textit{instance-dependent} label noise is challenging, because it is hard to model such real-world noise. Note that there are psychological and physiological evi…
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels
Songhua Wu, Xiaobo Xia, Tongliang Liu +5
Learning with noisy labels has attracted a lot of attention in recent years, where the mainstream approaches are in pointwise manners. Meanwhile, pairwise manners have shown great…
Multi-Class Classification from Noisy-Similarity-Labeled Data
Songhua Wu, Xiaobo Xia, Tongliang Liu +5
A similarity label indicates whether two instances belong to the same class while a class label shows the class of the instance. Without class labels, a multi-class classifier coul…