6 citations · 16 across the 17 of their papers we have counts for
5 papers · 1 filter
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Muyang Li, Runze Wu, Haoyu Liu +4
Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning…
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning
Zhuo Huang, Li Shen, Jun Yu +2
Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. However, most SSL methods are commonly based on ins…
Winning Prize Comes from Losing Tickets: Improve Invariant Learning by Exploring Variant Parameters for Out-of-Distribution Generalization
Zhuo Huang, Muyang Li, Li Shen +4
Out-of-Distribution (OOD) Generalization aims to learn robust models that generalize well to various environments without fitting to distribution-specific features. Recent studies…
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
Runqi Lin, Chaojian Yu, Bo Han +1
Overfitting negatively impacts the generalization ability of deep neural networks (DNNs) in both natural and adversarial training. Existing methods struggle to consistently address…
Understanding Robust Overfitting from the Feature Generalization Perspective
Chaojian Yu, Xiaolong Shi, Jun Yu +2
Adversarial training (AT) constructs robust neural networks by incorporating adversarial perturbations into natural data. However, it is plagued by the issue of robust overfitting…