20 citations · 134 across the 33 of their papers we have counts for
10 papers · 1 filter
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective
Yifei Wang, Liangchen Li, Jiansheng Yang +2
Adversarial Training (AT) has become arguably the state-of-the-art algorithm for extracting robust features. However, researchers recently notice that AT suffers from severe robust…
Robust Long-Tailed Learning via Label-Aware Bounded CVaR
Hong Zhu, Runpeng Yu, Xing Tang +3
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training. In…
On the Generalization of Multi-modal Contrastive Learning
Qi Zhang, Yifei Wang, Yisen Wang
Multi-modal contrastive learning (MMCL) has recently garnered considerable interest due to its superior performance in visual tasks, achieved by embedding multi-modal data, such as…
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…
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond
Xiaojun Guo, Yifei Wang, Tianqi Du +1
Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of chara…
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…
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Yifei Wang, Qi Zhang, Tianqi Du +3
In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning…
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism
Zhijian Zhuo, Yifei Wang, Jinwen Ma +1
Recently, a variety of methods under the name of non-contrastive learning (like BYOL, SimSiam, SwAV, DINO) show that when equipped with some asymmetric architectural designs, align…
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…
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
Yifei Wang, Yupan Wang, Zeyu Zhang +3
Unsupervised/self-supervised graph neural networks (GNN) are vulnerable to inherent randomness in the input graph data which greatly affects the performance of the model in downstr…