{"total":627,"rows":[{"id":"2103.14749","title":"Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks","created":"2021-03-26","cited_by_count":395,"area":null},{"id":"2110.12088","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","created":"2021-10-22","cited_by_count":256,"area":null},{"id":"2110.11328","title":"A Fine-Grained Analysis on Distribution Shift","created":"2021-10-21","cited_by_count":171,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2106.15853","title":"Understanding and Improving Early Stopping for Learning with Noisy Labels","created":"2021-06-30","cited_by_count":113,"area":null},{"id":"2203.04181","title":"Selective-Supervised Contrastive Learning with Noisy Labels","created":"2022-03-08","cited_by_count":112,"area":null},{"id":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102,"area":null},{"id":"2102.02400","title":"Provably End-to-end Label-Noise Learning without Anchor Points","created":"2021-02-04","cited_by_count":97,"area":null},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93,"area":null},{"id":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86,"area":null},{"id":"2202.12040","title":"Self-Training: A Survey","created":"2022-02-24","cited_by_count":86,"area":null},{"id":"2102.11628","title":"FINE Samples for Learning with Noisy Labels","created":"2021-02-23","cited_by_count":82,"area":null},{"id":"2110.06465","title":"Breaking the Dilemma of Medical Image-to-image Translation","created":"2021-10-13","cited_by_count":82,"area":null},{"id":"2103.13029","title":"Jo-SRC: A Contrastive Approach for Combating Noisy Labels","created":"2021-03-24","cited_by_count":80,"area":null},{"id":"2103.15916","title":"Robust Audio-Visual Instance Discrimination","created":"2021-03-29","cited_by_count":80,"area":null},{"id":"2106.00445","title":"Sample Selection with Uncertainty of Losses for Learning with Noisy Labels","created":"2021-06-01","cited_by_count":79,"area":null},{"id":"2203.14542","title":"UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning","created":"2022-03-28","cited_by_count":77,"area":null},{"id":"2105.04522","title":"Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels","created":"2021-05-10","cited_by_count":74,"area":null},{"id":"2106.04714","title":"NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs","created":"2021-06-08","cited_by_count":73,"area":null},{"id":"2106.04149","title":"To Smooth or Not? When Label Smoothing Meets Noisy Labels","created":"2021-06-08","cited_by_count":70,"area":null},{"id":"2103.13646","title":"Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels","created":"2021-03-25","cited_by_count":69,"area":null},{"id":"2109.03216","title":"Learning Fast Sample Re-weighting Without Reward Data","created":"2021-09-07","cited_by_count":69,"area":null},{"id":"2207.10299","title":"Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition","created":"2022-07-21","cited_by_count":67,"area":null},{"id":"2310.14664","title":"Data Pruning via Moving-one-Sample-out","created":"2023-10-23","cited_by_count":67,"area":null},{"id":"2102.05291","title":"Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels","created":"2021-02-10","cited_by_count":65,"area":null},{"id":"2102.02414","title":"Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization","created":"2021-02-04","cited_by_count":62,"area":null},{"id":"2106.10891","title":"Open-set Label Noise Can Improve Robustness Against Inherent Label Noise","created":"2021-06-21","cited_by_count":59,"area":null},{"id":"2203.14675","title":"Part-based Pseudo Label Refinement for Unsupervised Person Re-identification","created":"2022-03-28","cited_by_count":59,"area":null},{"id":"2108.11035","title":"NGC: A Unified Framework for Learning with Open-World Noisy Data","created":"2021-08-25","cited_by_count":58,"area":null},{"id":"2110.06283","title":"Detecting Corrupted Labels Without Training a Model to Predict","created":"2021-10-12","cited_by_count":58,"area":null},{"id":"2106.01606","title":"Exploring Memorization in Adversarial Training","created":"2021-06-03","cited_by_count":55,"area":null},{"id":"2202.02200","title":"Learning with Neighbor Consistency for Noisy Labels","created":"2022-02-04","cited_by_count":55,"area":null},{"id":"2108.04063","title":"Co-learning: Learning from Noisy Labels with Self-supervision","created":"2021-08-05","cited_by_count":51,"area":null},{"id":"2109.02986","title":"Instance-dependent Label-noise Learning under a Structural Causal Model","created":"2021-09-07","cited_by_count":50,"area":null},{"id":"2104.05514","title":"Self-Training with Weak Supervision","created":"2021-04-12","cited_by_count":49,"area":null},{"id":"2110.12911","title":"Instance-Dependent Partial Label Learning","created":"2021-10-25","cited_by_count":49,"area":null},{"id":"2206.02791","title":"Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation","created":"2022-06-06","cited_by_count":49,"area":null},{"id":"2103.14843","title":"From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation","created":"2021-03-27","cited_by_count":48,"area":null},{"id":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","cited_by_count":48,"area":null},{"id":"2112.03694","title":"Hard Sample Aware Noise Robust Learning for Histopathology Image Classification","created":"2021-12-05","cited_by_count":48,"area":null},{"id":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2301.13381","title":"When Source-Free Domain Adaptation Meets Learning with Noisy Labels","created":"2023-01-31","cited_by_count":47,"area":null},{"id":"2106.03110","title":"Asymmetric Loss Functions for Learning with Noisy Labels","created":"2021-06-06","cited_by_count":46,"area":null},{"id":"2201.12498","title":"Investigating Why Contrastive Learning Benefits Robustness Against Label Noise","created":"2022-01-29","cited_by_count":46,"area":null},{"id":"2201.04309","title":"Robust Contrastive Learning against Noisy Views","created":"2022-01-12","cited_by_count":45,"area":null},{"id":"2109.00574","title":"Active label cleaning for improved dataset quality under resource constraints","created":"2021-09-01","cited_by_count":44,"area":null},{"id":"2103.05254","title":"MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation","created":"2021-03-09","cited_by_count":43,"area":null},{"id":"2103.04173","title":"LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment","created":"2021-03-06","cited_by_count":40,"area":null},{"id":"2212.04055","title":"Mitigating Memorization of Noisy Labels by Clipping the Model Prediction","created":"2022-12-08","cited_by_count":40,"area":null},{"id":"2104.15092","title":"Faster Meta Update Strategy for Noise-Robust Deep Learning","created":"2021-04-30","cited_by_count":38,"area":null},{"id":"2201.11349","title":"Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift","created":"2022-01-27","cited_by_count":38,"area":null},{"id":"2104.08984","title":"Contrastive Learning Improves Model Robustness Under Label Noise","created":"2021-04-19","cited_by_count":36,"area":null},{"id":"2106.09291","title":"Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion","created":"2021-06-17","cited_by_count":36,"area":null},{"id":"2110.12257","title":"Game of Gradients: Mitigating Irrelevant Clients in Federated Learning","created":"2021-10-23","cited_by_count":35,"area":null},{"id":"2104.06574","title":"Joint Negative and Positive Learning for Noisy Labels","created":"2021-04-14","cited_by_count":34,"area":null},{"id":"2202.02016","title":"Identifiability of Label Noise Transition Matrix","created":"2022-02-04","cited_by_count":34,"area":null},{"id":"2206.02659","title":"Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees","created":"2022-06-06","cited_by_count":34,"area":null},{"id":"2208.11351","title":"Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization","created":"2022-08-24","cited_by_count":33,"area":null},{"id":"2101.02839","title":"Unsupervised Domain Adaptation of Black-Box Source Models","created":"2021-01-08","cited_by_count":32,"area":null},{"id":"2108.02399","title":"Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach","created":"2021-08-05","cited_by_count":32,"area":null},{"id":"2104.13766","title":"Boosting Co-teaching with Compression Regularization for Label Noise","created":"2021-04-28","cited_by_count":31,"area":null},{"id":"2105.13001","title":"Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network","created":"2021-05-27","cited_by_count":30,"area":null},{"id":"2203.02496","title":"Learning from Label Proportions by Learning with Label Noise","created":"2022-03-04","cited_by_count":30,"area":null},{"id":"2209.10448","title":"Uncertainty-aware Label Distribution Learning for Facial Expression Recognition","created":"2022-09-21","cited_by_count":30,"area":null},{"id":"2206.07181","title":"To Aggregate or Not? Learning with Separate Noisy Labels","created":"2022-06-14","cited_by_count":29,"area":null},{"id":"2211.06614","title":"Robust Training of Graph Neural Networks via Noise Governance","created":"2022-11-12","cited_by_count":29,"area":null},{"id":"2207.05471","title":"Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets","created":"2022-07-12","cited_by_count":28,"area":null},{"id":"2303.06930","title":"Twin Contrastive Learning with Noisy Labels","created":"2023-03-13","cited_by_count":28,"area":null},{"id":"2110.07735","title":"Continual Learning on Noisy Data Streams via Self-Purified Replay","created":"2021-10-14","cited_by_count":27,"area":null},{"id":"2202.01273","title":"Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features","created":"2022-02-02","cited_by_count":27,"area":null},{"id":"2303.12419","title":"BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency","created":"2023-03-22","cited_by_count":27,"area":null},{"id":"2110.09022","title":"Mitigating Memorization of Noisy Labels via Regularization between Representations","created":"2021-10-18","cited_by_count":26,"area":null},{"id":"2208.12807","title":"Towards Federated Learning against Noisy Labels via Local Self-Regularization","created":"2022-08-25","cited_by_count":26,"area":null},{"id":"2209.10015","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","created":"2022-09-20","cited_by_count":26,"area":null},{"id":"2302.02335","title":"Semi-Supervised Domain Adaptation with Source Label Adaptation","created":"2023-02-05","cited_by_count":26,"area":null},{"id":"2311.11202","title":"Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models","created":"2023-11-19","cited_by_count":26,"area":null},{"id":"2202.05613","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","created":"2022-02-11","cited_by_count":24,"area":null},{"id":"2205.01156","title":"SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels","created":"2022-05-02","cited_by_count":24,"area":null},{"id":"2311.01108","title":"Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance","created":"2023-11-02","cited_by_count":24,"area":null},{"id":"2101.12521","title":"Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification","created":"2021-01-29","cited_by_count":23,"area":null},{"id":"2103.16047","title":"Noise-resistant Deep Metric Learning with Ranking-based Instance Selection","created":"2021-03-30","cited_by_count":23,"area":null},{"id":"2106.03300","title":"Sum of Ranked Range Loss for Supervised Learning","created":"2021-06-07","cited_by_count":23,"area":null},{"id":"2206.00830","title":"Progressive Purification for Instance-Dependent Partial Label Learning","created":"2022-06-02","cited_by_count":23,"area":null},{"id":"2102.05336","title":"Understanding Instance-Level Label Noise: Disparate Impacts and Treatments","created":"2021-02-10","cited_by_count":22,"area":null},{"id":"2205.00690","title":"From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model","created":"2022-05-02","cited_by_count":22,"area":null},{"id":"2208.03207","title":"Neighborhood Collective Estimation for Noisy Label Identification and Correction","created":"2022-08-05","cited_by_count":22,"area":null},{"id":"2306.08194","title":"Learning on Graphs under Label Noise","created":"2023-06-14","cited_by_count":22,"area":null},{"id":"2310.17468","title":"Cross-modal Active Complementary Learning with Self-refining Correspondence","created":"2023-10-26","cited_by_count":22,"area":null},{"id":"2502.06905","title":"Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty","created":"2025-02-10","cited_by_count":22,"area":null},{"id":"2103.03414","title":"Unified Robust Training for Graph NeuralNetworks against Label Noise","created":"2021-03-05","cited_by_count":21,"area":null},{"id":"2106.07451","title":"Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2106.15292","title":"Adaptive Sample Selection for Robust Learning under Label Noise","created":"2021-06-29","cited_by_count":21,"area":null},{"id":"2201.08984","title":"PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning","created":"2022-01-22","cited_by_count":21,"area":null},{"id":"2203.01785","title":"On Learning Contrastive Representations for Learning with Noisy Labels","created":"2022-03-03","cited_by_count":21,"area":null},{"id":"2204.09371","title":"Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification","created":"2022-04-20","cited_by_count":21,"area":null},{"id":"2205.04723","title":"Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training","created":"2022-05-10","cited_by_count":21,"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":"2302.06810","title":"Learning from Noisy Labels with Decoupled Meta Label Purifier","created":"2023-02-14","cited_by_count":21,"area":null},{"id":"2105.10843","title":"Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation","created":"2021-05-23","cited_by_count":20,"area":null},{"id":"2106.06152","title":"On the Robustness of Average Losses for Partial-Label Learning","created":"2021-06-11","cited_by_count":20,"area":null}],"next_cursor":"100"}