{"total":20,"rows":[{"id":"2306.04160","title":"Rethinking Weak Supervision in Helping Contrastive Learning","created":"2023-06-07","cited_by_count":18,"area":null},{"id":"2504.02775","title":"TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection","created":"2025-04-03","cited_by_count":5,"area":null},{"id":"2403.02690","title":"Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning","created":"2024-03-05","cited_by_count":4,"area":null},{"id":"2404.06795","title":"Extracting Clean and Balanced Subset for Noisy Long-tailed Classification","created":"2024-04-10","cited_by_count":3,"area":null},{"id":"2403.10252","title":"Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning","created":"2024-03-15","cited_by_count":2,"area":null},{"id":"2409.03254","title":"Granular-ball Representation Learning for Deep CNN on Learning with Label Noise","created":"2024-09-05","cited_by_count":2,"area":null},{"id":"2401.13360","title":"Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels","created":"2024-01-24","cited_by_count":1,"area":null},{"id":"2503.11140","title":"Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation","created":"2025-03-14","cited_by_count":1,"area":null},{"id":"2504.14800","title":"A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions","created":"2025-04-21","cited_by_count":1,"area":null},{"id":"2510.08179","title":"Dual-granularity Sinkhorn Distillation for Enhanced Learning from Long-tailed Noisy Data","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2605.23254","title":"CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2403.02363","title":"Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2408.03977","title":"Learning from Noisy Labels for Long-tailed Data via Optimal Transport","created":"2024-08-07","cited_by_count":0,"area":null},{"id":"2410.09409","title":"Distribution-aware Noisy-label Crack Segmentation","created":"2024-10-12","cited_by_count":0,"area":null},{"id":"2506.03229","title":"Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2506.05721","title":"Any-Class Presence Likelihood for Robust Multi-Label Classification with Abundant Negative Data","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2511.02738","title":"Calibration improves detection of mislabeled examples","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2603.19722","title":"FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2604.23125","title":"Learning from Imperfect Text Guidance: Robust Long-Tail Visual Recognition with High-Noise Label","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2605.22591","title":"Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure","created":"2026-05-21","cited_by_count":0,"area":null}],"next_cursor":null}