{"total":108,"rows":[{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802,"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":"2103.07756","title":"Learning with Feature-Dependent Label Noise: A Progressive Approach","created":"2021-03-13","cited_by_count":102,"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":"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":"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":"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":"2202.02200","title":"Learning with Neighbor Consistency for Noisy Labels","created":"2022-02-04","cited_by_count":55,"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":"2204.00613","title":"On the Importance of Asymmetry for Siamese Representation Learning","created":"2022-04-01","cited_by_count":39,"area":null},{"id":"2407.21794","title":"Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey","created":"2024-07-31","cited_by_count":36,"area":"mllm"},{"id":"2110.12257","title":"Game of Gradients: Mitigating Irrelevant Clients in Federated Learning","created":"2021-10-23","cited_by_count":35,"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":"2209.10015","title":"Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics","created":"2022-09-20","cited_by_count":26,"area":null},{"id":"2211.00882","title":"DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network","created":"2022-11-02","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":"2103.16047","title":"Noise-resistant Deep Metric Learning with Ranking-based Instance Selection","created":"2021-03-30","cited_by_count":23,"area":null},{"id":"2204.05494","title":"Few-shot Learning with Noisy Labels","created":"2022-04-12","cited_by_count":23,"area":null},{"id":"2107.08943","title":"OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data","created":"2021-06-29","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":"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":"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":"2305.06024","title":"A Survey on the Robustness of Computer Vision Models against Common Corruptions","created":"2023-05-10","cited_by_count":21,"area":null},{"id":"2301.12321","title":"Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data","created":"2023-01-29","cited_by_count":20,"area":null},{"id":"2305.19518","title":"Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels","created":"2023-05-31","cited_by_count":20,"area":"rag"},{"id":"2102.03482","title":"Understanding the Interaction of Adversarial Training with Noisy Labels","created":"2021-02-06","cited_by_count":19,"area":null},{"id":"2203.15202","title":"SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation","created":"2022-03-29","cited_by_count":17,"area":null},{"id":"2209.00906","title":"Instance-Dependent Noisy Label Learning via Graphical Modelling","created":"2022-09-02","cited_by_count":17,"area":null},{"id":"2308.13862","title":"Late Stopping: Avoiding Confidently Learning from Mislabeled Examples","created":"2023-08-26","cited_by_count":17,"area":null},{"id":"2203.07788","title":"Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels","created":"2022-03-15","cited_by_count":15,"area":null},{"id":"2107.11413","title":"An Instance-Dependent Simulation Framework for Learning with Label Noise","created":"2021-07-23","cited_by_count":14,"area":null},{"id":"2110.13699","title":"Addressing out-of-distribution label noise in webly-labelled data","created":"2021-10-26","cited_by_count":14,"area":null},{"id":"2211.05764","title":"DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems","created":"2022-11-09","cited_by_count":14,"area":null},{"id":"2305.17895","title":"ReSup: Reliable Label Noise Suppression for Facial Expression Recognition","created":"2023-05-29","cited_by_count":14,"area":null},{"id":"2404.18191","title":"Exploring the Robustness of In-Context Learning with Noisy Labels","created":"2024-04-28","cited_by_count":14,"area":null},{"id":"2111.11288","title":"SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise","created":"2021-11-22","cited_by_count":13,"area":null},{"id":"2410.21331","title":"Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness","created":"2024-10-27","cited_by_count":13,"area":null},{"id":"2101.05467","title":"Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model","created":"2021-01-14","cited_by_count":12,"area":null},{"id":"2212.03462","title":"PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels","created":"2022-12-07","cited_by_count":10,"area":null},{"id":"2309.05145","title":"Outlier Robust Adversarial Training","created":"2023-09-10","cited_by_count":10,"area":null},{"id":"2302.09595","title":"Latent Class-Conditional Noise Model","created":"2023-02-19","cited_by_count":9,"area":null},{"id":"2305.04203","title":"Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning","created":"2023-05-07","cited_by_count":8,"area":null},{"id":"2305.10709","title":"NoisywikiHow: A Benchmark for Learning with Real-world Noisy Labels in Natural Language Processing","created":"2023-05-18","cited_by_count":8,"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":"2404.04159","title":"Noisy Label Processing for Classification: A Survey","created":"2024-04-05","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":"2106.00455","title":"Instance Correction for Learning with Open-set Noisy Labels","created":"2021-06-01","cited_by_count":7,"area":null},{"id":"2205.07611","title":"Noise-Tolerant Learning for Audio-Visual Action Recognition","created":"2022-05-16","cited_by_count":7,"area":null},{"id":"2312.15571","title":"A Survey on Open-Set Image Recognition","created":"2023-12-25","cited_by_count":7,"area":null},{"id":"2105.03059","title":"Self-paced Resistance Learning against Overfitting on Noisy Labels","created":"2021-05-07","cited_by_count":6,"area":null},{"id":"2204.09120","title":"Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives","created":"2022-04-18","cited_by_count":6,"area":null},{"id":"2207.01573","title":"Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets","created":"2022-07-04","cited_by_count":6,"area":null},{"id":"2401.06989","title":"Gradient Coreset for Federated Learning","created":"2024-01-13","cited_by_count":6,"area":null},{"id":"2505.03809","title":"When Dynamic Data Selection Meets Data Augmentation","created":"2025-05-02","cited_by_count":6,"area":null},{"id":"2103.10869","title":"MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels","created":"2021-03-19","cited_by_count":5,"area":null},{"id":"2207.00118","title":"ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State","created":"2022-06-30","cited_by_count":5,"area":null},{"id":"2501.06678","title":"Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels","created":"2025-01-12","cited_by_count":5,"area":null},{"id":"2103.12814","title":"Co-matching: Combating Noisy Labels by Augmentation Anchoring","created":"2021-03-23","cited_by_count":4,"area":null},{"id":"2104.04665","title":"On Universal Black-Box Domain Adaptation","created":"2021-04-10","cited_by_count":4,"area":null},{"id":"2106.14344","title":"Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks","created":"2021-06-28","cited_by_count":4,"area":null},{"id":"2301.11174","title":"Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data","created":"2023-01-26","cited_by_count":4,"area":null},{"id":"2303.09470","title":"Learning with Noisy Labels through Learnable Weighting and Centroid Similarity","created":"2023-03-16","cited_by_count":4,"area":null},{"id":"2401.04390","title":"Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations","created":"2024-01-09","cited_by_count":4,"area":null},{"id":"2103.13569","title":"Approximating Instance-Dependent Noise via Instance-Confidence Embedding","created":"2021-03-25","cited_by_count":3,"area":null},{"id":"2104.09045","title":"Do We Really Need Gold Samples for Sample Weighting Under Label Noise?","created":"2021-04-19","cited_by_count":3,"area":null},{"id":"2210.01891","title":"Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift","created":"2022-10-04","cited_by_count":3,"area":null},{"id":"2310.09761","title":"CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes","created":"2023-10-15","cited_by_count":3,"area":null},{"id":"2411.01613","title":"ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy Labels","created":"2024-11-03","cited_by_count":3,"area":null},{"id":"2501.01130","title":"An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise","created":"2025-01-02","cited_by_count":2,"area":null},{"id":"2604.03993","title":"Can LLMs Learn to Reason Robustly under Noisy Supervision?","created":"2026-04-05","cited_by_count":2,"area":"lr"},{"id":"2202.08436","title":"PENCIL: Deep Learning with Noisy Labels","created":"2022-02-17","cited_by_count":1,"area":null},{"id":"2206.09375","title":"Gray Learning from Non-IID Data with Out-of-distribution Samples","created":"2022-06-19","cited_by_count":1,"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":"2301.00545","title":"Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels","created":"2023-01-02","cited_by_count":1,"area":null},{"id":"2302.00995","title":"Open-Set Multi-Source Multi-Target Domain Adaptation","created":"2023-02-02","cited_by_count":1,"area":null},{"id":"2306.05046","title":"A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels","created":"2023-06-08","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":"2308.01184","title":"Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation","created":"2023-08-02","cited_by_count":1,"area":null},{"id":"2406.12293","title":"Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification","created":"2024-06-18","cited_by_count":1,"area":null},{"id":"2407.07673","title":"Towards Adaptive Pseudo-label Learning for Semi-Supervised Temporal Action Localization","created":"2024-07-10","cited_by_count":1,"area":null},{"id":"2410.04256","title":"Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels","created":"2024-10-05","cited_by_count":1,"area":null},{"id":"2501.04269","title":"Open set label noise learning with robust sample selection and margin-guided module","created":"2025-01-08","cited_by_count":1,"area":null},{"id":"2501.11063","title":"Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection","created":"2025-01-19","cited_by_count":1,"area":null},{"id":"2101.09412","title":"Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones","created":"2021-01-23","cited_by_count":0,"area":null},{"id":"2103.00813","title":"DST: Data Selection and joint Training for Learning with Noisy Labels","created":"2021-03-01","cited_by_count":0,"area":null},{"id":"2106.15185","title":"INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks","created":"2021-06-29","cited_by_count":0,"area":null},{"id":"2107.09735","title":"kNet: A Deep kNN Network To Handle Label Noise","created":"2021-07-20","cited_by_count":0,"area":null},{"id":"2107.10504","title":"External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery","created":"2021-07-22","cited_by_count":0,"area":null},{"id":"2108.01431","title":"Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering","created":"2021-08-03","cited_by_count":0,"area":null},{"id":"2109.03748","title":"A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training","created":"2021-09-08","cited_by_count":0,"area":null},{"id":"2109.14563","title":"Robust Temporal Ensembling for Learning with Noisy Labels","created":"2021-09-29","cited_by_count":0,"area":null},{"id":"2206.13025","title":"Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels","created":"2022-06-27","cited_by_count":0,"area":null},{"id":"2212.02895","title":"Training Neural Networks on Data Sources with Unknown Reliability","created":"2022-12-06","cited_by_count":0,"area":null},{"id":"2307.00553","title":"Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples","created":"2023-07-02","cited_by_count":0,"area":null},{"id":"2307.08319","title":"Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data","created":"2023-07-17","cited_by_count":0,"area":null},{"id":"2307.08596","title":"Omnipotent Adversarial Training in the Wild","created":"2023-07-14","cited_by_count":0,"area":null},{"id":"2405.01186","title":"Potential Energy based Mixture Model for Noisy Label Learning","created":"2024-05-02","cited_by_count":0,"area":null}],"next_cursor":"100"}