{"total":17,"rows":[{"id":"2110.04596","title":"Deep Long-Tailed Learning: A Survey","created":"2021-10-09","cited_by_count":417,"area":null},{"id":"2305.05230","title":"FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity","created":"2023-05-09","cited_by_count":33,"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":"2207.13378","title":"Identifying Hard Noise in Long-Tailed Sample Distribution","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2208.09833","title":"Label-Noise Learning with Intrinsically Long-Tailed Data","created":"2022-08-21","cited_by_count":17,"area":null},{"id":"2205.12702","title":"Detecting Label Errors by using Pre-Trained Language Models","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2110.11553","title":"Prototypical Classifier for Robust Class-Imbalanced Learning","created":"2021-10-22","cited_by_count":10,"area":null},{"id":"2207.00208","title":"e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce","created":"2022-07-01","cited_by_count":10,"area":null},{"id":"2304.00101","title":"SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail","created":"2023-03-31","cited_by_count":9,"area":null},{"id":"2303.12291","title":"Fairness Improves Learning from Noisily Labeled Long-Tailed Data","created":"2023-03-22","cited_by_count":5,"area":null},{"id":"2311.11293","title":"From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web","created":"2023-11-19","cited_by_count":5,"area":null},{"id":"2502.16129","title":"Robust Dynamic Facial Expression Recognition","created":"2025-02-22","cited_by_count":4,"area":null},{"id":"2211.10906","title":"Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss","created":"2022-11-20","cited_by_count":2,"area":null},{"id":"2208.08132","title":"Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2307.16614","title":"LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels","created":"2023-07-31","cited_by_count":1,"area":null},{"id":"2211.13435","title":"A Benchmark of Long-tailed Instance Segmentation with Noisy Labels","created":"2022-11-24","cited_by_count":0,"area":null},{"id":"2410.21885","title":"Self-Relaxed Joint Training: Sample Selection for Severity Estimation with Ordinal Noisy Labels","created":"2024-10-29","cited_by_count":0,"area":null}],"next_cursor":null}