Label noise in multiclass classification is a major obstacle to the deployment of learning systems. However, unlike the widely used class-conditional noise (CCN) assumption that the noisy label is independent of the input feature given the true label, label noise in real-world datasets can be aleatory and heavily dependent on individual instances. In this work, we investigate the instance-dependent noise (IDN) model and propose an efficient approximation of IDN to capture the instance-specific label corruption. Concretely, noting the fact that most columns of the IDN transition matrix have only limited influence on the class-posterior estimation, we propose a variational approximation that uses a single-scalar confidence parameter. To cope with the situation where the mapping from the instance to its confidence value could vary significantly for two adjacent instances, we suggest using instance embedding that assigns a trainable parameter to each instance. The resulting instance-confidence embedding (ICE) method not only performs well under label noise but also can effectively detect ambiguous or mislabeled instances. We validate its utility on various image and text classification tasks.
Confidence Scores Make Instance-dependent Label-noise Learning Possible
RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Hong Kong Baptist University（香港浸会大学）；University of Sydney（悉尼大学）；University of Tokyo（东京大学）
DBC-Forest: Deep forest with binning confidence screening
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；Hebei Key Laboratory of Big Data Computing（河北省大数据计算重点实验室）；School of Economics and Management, Hebei University of Technology（河北工业大学经济管理学院）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology（河北工业大学省部共建电工装备可靠性与智能化国家重点实验室）；Alibaba Group（阿里巴巴集团）
Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Center for Computational Mathematics, Flatiron Institute（计算数学中心，Flatiron研究所）；Department of Applied Physics and Applied Mathematics, Columbia University（应用物理与应用数学系，哥伦比亚大学）
Peter the Great St.Petersburg Polytechnic University (SPbPU)（彼得大帝圣彼得堡理工大学（SPbPU））；St.Petersburg Clinical Research Center for Special Types of Medical Care (Oncology-oriented)（圣彼得堡特殊医疗类型临床研究中心（肿瘤导向））
Conformal Prediction Sets for Instance Segmentation
Laboratory for Information and Decision Systems, MIT（麻省理工学院信息与决策系统实验室）；Department of Electrical Engineering and Computer Science, MIT（麻省理工学院电子工程与计算机科学系）；Department of Mechanical Engineering, MIT（麻省理工学院机械工程系）；Institute for Data, Systems, and Society, MIT（麻省理工学院数据、系统与社会研究所）
Filtering instances and rejecting predictions to obtain reliable models in healthcare
Instituto de Computação（计算研究所）；Universidade Estadual de Campinas（Campinas州立大学）；Department of Informatics（信息学院）；King’s College London（伦敦国王学院）；Núcleo de Telessaúde（远程医疗中心）；Universidade Federal do Rio Grande do Sul（鲁汶联邦大学）；Escola Paulista de Medicina（巴西圣保罗医学学院）；Universidade Federal de São Paulo（圣保罗联邦大学）；Divisão de Ciência da Computação（计算机科学部）；Instituto Tecnológico de Aeronáutica（航空技术研究所）
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima
Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam（计算机科学与工程学院，胡志明市技术大学（HCMUT），越南）；Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam（越南胡志明市国家大学（VNU-HCM），越南）
Joint Bootstrapping Machines for High Confidence Relation Extraction
Siemens AG（西门子股份公司）；University of Munich (LMU)（慕尼黑大学（LMU））
用于从文本中提取关系的半监督自举技术迭代地扩展一组初始种子实例。由于缺乏标记数据，自举中的一个关键挑战是语义漂移：如果在迭代期间添加了假阳性实例，则所有后续迭代都将受到污染。我们介绍BREX，一个新的自举方法，防止这种污染的高度有效的信心评估。这是通过联合使用实体和模板种子（而不是在以前的工作中只有一个），通过并行扩展实体和模板，并在每次迭代中相互约束的方式，并通过引入更高质量的模板相似性措施。实验结果表明，BREX在四个关系中实现了比现有技术好0.13（0.87 vs. 0.74）的F1。
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
Hangzhou Dianzi University（杭州电子科技大学）；University of Technology Sydney（悉尼科技大学）；Huazhong University of Science and Technology（华中科技大学）；Renmin University of China（中国人民大学）
Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification
Birla Institute of Technology and Science, Pilani（毕拉理工与科学大学 Pilani校区）；Macquarie University（麦考瑞大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）；TCS Research（塔塔咨询服务公司研究院）
Confidence-aware Contrastive Learning for Selective Classification
National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；College of Computer Science and Software Engineering, Hohai University（河海大学计算机与软件学院）；Key Laboratory of Water Big Data Technology of Ministry of Water Resources（水利部水大数据技术重点实验室）
