13 citations · 20 across the 7 of their papers we have counts for
5 papers
Rethinking Weak Supervision in Helping Contrastive Learning
Jingyi Cui, Weiran Huang, Yifei Wang +1
Contrastive learning has shown outstanding performances in both supervised and unsupervised learning, and has recently been introduced to solve weakly supervised learning problems…
Contrastive Label Enhancement
Yifei Wang, Yiyang Zhou, Jihua Zhu +3
Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are foc…
CFA: Class-wise Calibrated Fair Adversarial Training
Zeming Wei, Yifei Wang, Yiwen Guo +1
Adversarial training has been widely acknowledged as the most effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). S…
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
Rundong Luo, Yifei Wang, Yisen Wang
Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervis…
A Systematic Security Evaluation of Android's Multi-User Framework
Paul Ratazzi, Yousra Aafer, Amit Ahlawat +3
Like many desktop operating systems in the 1990s, Android is now in the process of including support for multi-user scenarios. Because these scenarios introduce new threats to the…