4 citations · 7 across the 5 of their papers we have counts for
5 papers
Binary Classification with Confidence Difference
Wei Wang, Lei Feng, Yuchen Jiang +3
Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. Howev…
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Yuhao Wu, Xiaobo Xia, Jun Yu +4
Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high. The remarkable progress in workin…
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Ming-Kun Xie, Jia-Hao Xiao, Hao-Zhe Liu +3
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional…
Assessing Vulnerabilities of Adversarial Learning Algorithm through Poisoning Attacks
Jingfeng Zhang, Bo Song, Bo Han +3
Adversarial training (AT) is a robust learning algorithm that can defend against adversarial attacks in the inference phase and mitigate the side effects of corrupted data in the t…
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu +4
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning. Most prior works treat either problem in an i…