Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels. It also shows the advantage in maintaining more training samples than previous methods. Then, we power this entropy criterion with the Collaborative Label Correction (CLC) framework to further avoid undesired local minimums of the single network. A range of experiments have been conducted on multiple benchmarks with both synthetic and real-world settings. Extensive results indicate that our CLC outperforms several state-of-the-art methods.
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；The University of Tokyo（东京大学）
Learning from Noisy Labels via Dynamic Loss Thresholding
Huawei Technologies（华为技术有限公司）；Southeast University（东南大学）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education（计算机网络和信息集成教育部重点实验室（东南大学））；Collaborative Innovation Center of Wireless Communications Technology（无线通信技术协同创新中心）
SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning
School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications（高性能语言信息处理与云计算应用北京工程研究中心）；Southeast Academy of Information Technology, Beijing Institute of Technology（信息科技东南研究院，北京理工大学）；Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究院）；Starsee
Joint Laboratory of Data Science and Business Intelligence, School of Statistics, Southwestern University of Finance and Economics（数据科学与商业智能联合实验室，统计学院，西南财经大学）；Department of Electrical Engineering and Computer Science, University of Kansas（电气工程与计算机科学系，堪萨斯大学）；JD Intelligent Cities Research and JD Intelligent Cities Business Unit（京东智能城市研究部和京东智能城市业务部）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算与人工智能学院，西南交通大学）
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Microsoft Research Asia（微软亚洲研究院）；Tokyo Institute of Technology（东京工业大学）；Carnegie Mellon University（卡内基梅隆大学）；North Carolina State University（北卡罗来纳州立大学）；Microsoft STCA（微软（中国）有限公司服务器与工具事业部）；Max-Planck-Institut für Informatik（马克斯·普朗克信息学研究所）；Nanjing University（南京大学）
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Southwest University（西南大学）；China University of Geosciences（中国地质大学）；George Mason University（乔治梅森大学）；Deakin University（迪肯大学）；Beijing University of Civil Engineering and Architecture（北京建筑大学）
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
London South Bank University（伦敦南岸大学）；Instituto de Telecomunicações（电信研究所）
CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction
School of Mathematics, Jilin University（吉林大学数学学院）；AI for Science and Engineering Center, Shenzhen Loop Area Institute（深圳循环区研究院科学与工程人工智能中心）；BGP Inc., China National Petroleum Corporation（中国石油天然气集团公司东方地球物理公司）；National Engineering Research Center of Oil & Gas Exploration Computer Software（油气勘探计算机软件国家工程研究中心）
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks
University of Technology Sydney（悉尼科技大学）；University of Engineering and Technology, Vietnam National University, Hanoi（河内越南国立大学工程技术大学）；Advanced Institute of Engineering and Technology (AVITECH)（高等工程技术研究院（AVITECH））
University of Kassel（卡塞尔大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；University of Kaiserslautern（凯泽斯劳滕大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Deep Reinforcement Learning-Based User Scheduling for Collaborative Perception
National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究实验室）；Purple Mountain Laboratories（紫金山实验室）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）
