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ASCL: Adversarial supervised contrastive learning for defense against word substitution attacks 期刊论文
NEUROCOMPUTING, 2022, 卷号: 510, 页码: 59-68
作者:  Shi, Jiahui;  Li, Linjing;  Zeng, Daniel
Adobe PDF(1054Kb)  |  收藏  |  浏览/下载：250/28  |  提交时间：2022/11/14
Adversarial example  Adversarial training  Model robustness  Contrastive learning  Natural language processing  
Narrowing the Gap: Improved Detector Training With Noisy Location Annotations 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 6369-6380
作者:  Wang, Shaoru;  Gao, Jin;  Li, Bing;  Hu, Weiming
Adobe PDF(1489Kb)  |  收藏  |  浏览/下载：237/30  |  提交时间：2022/11/14
Annotations  Noise measurement  Detectors  Task analysis  Training  Object detection  Degradation  Object detection  noisy label  Bayesian estimation  teacher-student learning  
Weakly-Supervised Facial Expression Recognition in the Wild With Noisy Data 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 1800-1814
作者:  Zhang, Feifei;  Xu, Mingliang;  Xu, Changsheng
收藏  |  浏览/下载：250/0  |  提交时间：2022/06/10
Noise measurement  Face recognition  Data models  Task analysis  Training data  Training  Annotations  Facial expression recognition  noisy labeled data  clean labels  end-to-end  pose modeling  noise modeling  
Seek Common Ground While Reserving Differences: A Model-Agnostic Module for Noisy Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 1020-1030
作者:  Zuo, Yukun;  Yao, Hantao;  Zhuang, Liansheng;  Xu, Changsheng
收藏  |  浏览/下载：229/0  |  提交时间：2022/06/06
Noise measurement  Adaptation models  Predictive models  Reliability  Task analysis  Standards  Data models  Noisy domain adaptation  Seek common ground component  Reserve differences component