Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks. Cleaning data manually is labour-intensive and time-consuming. Previous research mostly focuses on enhancing classification models against noisy labels, while the robustness of deep metric learning (DML) against noisy labels remains less well-explored. In this paper, we bridge this important gap by proposing Probabilistic Ranking-based Instance Selection with Memory (PRISM) approach for DML. PRISM calculates the probability of a label being clean, and filters out potentially noisy samples. Specifically, we propose a novel method, namely the von Mises-Fisher Distribution Similarity (vMF-Sim), to calculate this probability by estimating a von Mises-Fisher (vMF) distribution for each data class. Compared with the existing average similarity method (AvgSim), vMF-Sim considers the variance of each class in addition to the average similarity. With such a design, the proposed approach can deal with challenging DML situations in which the majority of the samples are noisy. Extensive experiments on both synthetic and real-world noisy dataset show that the proposed approach achieves up to 8.37% higher Precision@1 compared with the best performing state-of-the-art baseline approaches, within reasonable training time.
Probabilistic Domain Adaptation for Biomedical Image Segmentation
Georg-August-University Göttingen（哥廷根大学）；Institute of Computer Science（计算机科学学院）；Campus Institute Data Science (CIDAS)（校园数据科学研究所）；CAIMed - Lower Saxony Center for AI & Causal Methods in Medicine（下萨克森人工智能与因果医学中心）；Cluster of Excellence Multiscale Bioimaging (MBExC)（多尺度生物成像卓越中心）
Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；The Chinese University of Hong Kong（香港中文大学）；AT&T Labs Research（AT&T实验室研究部）
Probabilistic data quality assessment for structural monitoring data via outlier-resistant conditional diffusion model
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology（工业和信息化部智能防灾减灾重点实验室）；School of Civil Engineering, Harbin Institute of Technology（哈尔滨工业大学土木工程学院）；Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education（教育部结构动力行为与控制重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
Likelihood Hacking in Probabilistic Program Synthesis
University of Oxford, Department of Computer Science（牛津大学计算机科学系）；University of Edinburgh, School of Informatics（爱丁堡大学信息学院）；CIFAR Fellow, Learning in Machines and Brains（CIFAR Fellow, 机器与大脑学习）
