24 citations · 64 across the 17 of their papers we have counts for
7 papers · 1 filter
Out-of-distribution Detection with Implicit Outlier Transformation
Qizhou Wang, Junjie Ye, Feng Liu +5
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typi…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Jianing Zhu, Jiangchao Yao, Tongliang Liu +3
Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adve…
Latent Class-Conditional Noise Model
Jiangchao Yao, Bo Han, Zhihan Zhou +2
Learning with noisy labels has become imperative in the Big Data era, which saves expensive human labors on accurate annotations. Previous noise-transition-based methods have achie…
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…
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Xiong Peng, Feng Liu, Jingfen Zhang +4
Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training dat…