{"total":15,"rows":[{"id":"2312.09505","title":"Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning","created":"2023-12-15","cited_by_count":22,"area":null},{"id":"2211.10955","title":"When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method","created":"2022-11-20","cited_by_count":21,"area":null},{"id":"2208.08464","title":"CTRL: Clustering Training Losses for Label Error Detection","created":"2022-08-17","cited_by_count":17,"area":null},{"id":"2312.08852","title":"ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance","created":"2023-12-13","cited_by_count":11,"area":null},{"id":"2403.13417","title":"Diversified and Personalized Multi-rater Medical Image Segmentation","created":"2024-03-20","cited_by_count":9,"area":null},{"id":"2309.00894","title":"Regularly Truncated M-estimators for Learning with Noisy Labels","created":"2023-09-02","cited_by_count":8,"area":null},{"id":"2403.09975","title":"Skeleton-Based Human Action Recognition with Noisy Labels","created":"2024-03-15","cited_by_count":8,"area":null},{"id":"2410.05577","title":"Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future","created":"2024-10-08","cited_by_count":8,"area":null},{"id":"2402.11242","title":"Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection","created":"2024-02-17","cited_by_count":6,"area":null},{"id":"2512.10573","title":"Is the Information Bottleneck Robust Enough? Towards Label-Noise Resistant Information Bottleneck Learning","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2501.01470","title":"Balance-aware Sequence Sampling Makes Multi-modal Learning Better","created":"2025-01-01","cited_by_count":2,"area":null},{"id":"2403.15694","title":"Group Benefits Instances Selection for Data Purification","created":"2024-03-23","cited_by_count":0,"area":null},{"id":"2404.04800","title":"Coordinated Sparse Recovery of Label Noise","created":"2024-04-07","cited_by_count":0,"area":null},{"id":"2409.18147","title":"SSP-RACL: Classification of Noisy Fundus Images with Self-Supervised Pretraining and Robust Adaptive Credal Loss","created":"2024-09-25","cited_by_count":0,"area":null},{"id":"2610.05918","title":"Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels","created":"2026-10-05","cited_by_count":0,"area":null}],"next_cursor":null}