{"total":108,"rows":[{"id":"2206.09426","title":"ADBench: Anomaly Detection Benchmark","created":"2022-06-19","cited_by_count":232,"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":"2205.12679","title":"Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning","created":"2022-05-25","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":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2206.02280","title":"Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future","created":"2022-06-05","cited_by_count":45,"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":"2205.01500","title":"Meta Learning for Natural Language Processing: A Survey","created":"2022-05-03","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":"2304.06275","title":"Noisy Correspondence Learning with Meta Similarity Correction","created":"2023-04-13","cited_by_count":26,"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":"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":"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":"2104.07908","title":"MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning","created":"2021-04-16","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":"2307.11978","title":"Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?","created":"2023-07-22","cited_by_count":21,"area":"mllm"},{"id":"2111.14932","title":"Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection","created":"2021-11-29","cited_by_count":20,"area":null},{"id":"2310.05674","title":"Making Scalable Meta Learning Practical","created":"2023-10-09","cited_by_count":17,"area":null},{"id":"2207.14476","title":"Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels","created":"2022-07-29","cited_by_count":16,"area":null},{"id":"2306.09467","title":"AQuA: A Benchmarking Tool for Label Quality Assessment","created":"2023-06-15","cited_by_count":16,"area":null},{"id":"2402.17517","title":"Label-Noise Robust Diffusion Models","created":"2024-02-27","cited_by_count":16,"area":"diffusion"},{"id":"2208.11290","title":"ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels","created":"2022-08-24","cited_by_count":15,"area":"moe"},{"id":"2303.02404","title":"Fine-Grained Classification with Noisy Labels","created":"2023-03-04","cited_by_count":15,"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":"2111.04239","title":"Learning to Rectify for Robust Learning with Noisy Labels","created":"2021-11-08","cited_by_count":12,"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":"2202.04291","title":"L2B: Learning to Bootstrap Robust Models for Combating Label Noise","created":"2022-02-09","cited_by_count":10,"area":null},{"id":"2302.06805","title":"Learning with Noisy labels via Self-supervised Adversarial Noisy Masking","created":"2023-02-14","cited_by_count":10,"area":null},{"id":"2403.08618","title":"SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustness","created":"2024-03-13","cited_by_count":10,"area":"safety"},{"id":"2107.02378","title":"Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks","created":"2021-07-06","cited_by_count":9,"area":null},{"id":"2111.02570","title":"CLUES: Few-Shot Learning Evaluation in Natural Language Understanding","created":"2021-11-04","cited_by_count":9,"area":null},{"id":"2210.01213","title":"Robust Active Distillation","created":"2022-10-03","cited_by_count":9,"area":"distill"},{"id":"2212.09321","title":"Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features","created":"2022-12-19","cited_by_count":9,"area":null},{"id":"2312.07331","title":"Coupled Confusion Correction: Learning from Crowds with Sparse Annotations","created":"2023-12-12","cited_by_count":9,"area":null},{"id":"2305.17442","title":"Weaker Than You Think: A Critical Look at Weakly Supervised Learning","created":"2023-05-27","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":"2205.07611","title":"Noise-Tolerant Learning for Audio-Visual Action Recognition","created":"2022-05-16","cited_by_count":7,"area":null},{"id":"2404.16307","title":"Boosting Model Resilience via Implicit Adversarial Data Augmentation","created":"2024-04-25","cited_by_count":7,"area":null},{"id":"2210.11318","title":"A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture","created":"2022-10-20","cited_by_count":6,"area":null},{"id":"2211.12506","title":"Dynamic Loss For Robust Learning","created":"2022-11-22","cited_by_count":6,"area":null},{"id":"2212.13767","title":"Learning to Detect Noisy Labels Using Model-Based Features","created":"2022-12-28","cited_by_count":6,"area":null},{"id":"2302.08180","title":"Cross Modal Distillation for Flood Extent Mapping","created":"2023-02-16","cited_by_count":6,"area":"distill"},{"id":"2305.12961","title":"Enhanced Meta Label Correction for Coping with Label Corruption","created":"2023-05-22","cited_by_count":6,"area":null},{"id":"2412.18342","title":"Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization","created":"2024-12-24","cited_by_count":6,"area":null},{"id":"2505.00812","title":"Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization","created":"2025-05-01","cited_by_count":6,"area":null},{"id":"2408.14358","title":"An Embedding is Worth a Thousand Noisy Labels","created":"2024-08-26","cited_by_count":5,"area":null},{"id":"2303.14768","title":"Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies","created":"2023-03-26","cited_by_count":4,"area":null},{"id":"2305.07892","title":"DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning","created":"2023-05-13","cited_by_count":4,"area":null},{"id":"2307.04868","title":"Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise","created":"2023-07-10","cited_by_count":4,"area":null},{"id":"2508.17232","title":"Curvature Learning for Generalization of Hyperbolic Neural Networks","created":"2025-08-24","cited_by_count":4,"area":null},{"id":"2109.12457","title":"Learning to Selectively Learn for Weakly-supervised Paraphrase Generation","created":"2021-09-25","cited_by_count":3,"area":null},{"id":"2211.14451","title":"GLAMI-1M: A Multilingual Image-Text Fashion Dataset","created":"2022-11-17","cited_by_count":3,"area":null},{"id":"2401.14107","title":"Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement","created":"2024-01-25","cited_by_count":3,"area":null},{"id":"2412.02373","title":"Active Negative Loss: A Robust Framework for Learning with Noisy Labels","created":"2024-12-03","cited_by_count":3,"area":null},{"id":"2504.03783","title":"FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training","created":"2025-04-03","cited_by_count":3,"area":null},{"id":"2205.01310","title":"FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learning","created":"2022-05-03","cited_by_count":2,"area":null},{"id":"2310.06059","title":"Early Warning Prediction with Automatic Labeling in Epilepsy Patients","created":"2023-10-09","cited_by_count":2,"area":null},{"id":"2310.15533","title":"Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2408.16029","title":"Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis","created":"2024-08-28","cited_by_count":2,"area":null},{"id":"2508.06346","title":"Introducing Fractional Classification Loss for Robust Learning with Noisy Labels","created":"2025-08-08","cited_by_count":2,"area":null},{"id":"2510.10149","title":"Robust Learning of Diffusion Models with Extremely Noisy Conditions","created":"2025-10-11","cited_by_count":2,"area":"diffusion"},{"id":"2511.21500","title":"Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation","created":"2025-11-26","cited_by_count":2,"area":null},{"id":"2103.15055","title":"Friends and Foes in Learning from Noisy Labels","created":"2021-03-28","cited_by_count":1,"area":null},{"id":"2108.12603","title":"WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding","created":"2021-08-28","cited_by_count":1,"area":null},{"id":"2111.12498","title":"Meta Mask Correction for Nuclei Segmentation in Histopathological Image","created":"2021-11-24","cited_by_count":1,"area":null},{"id":"2303.08103","title":"Multi-task Meta Label Correction for Time Series Prediction","created":"2023-03-09","cited_by_count":1,"area":null},{"id":"2303.10802","title":"PASS: Peer-Agreement based Sample Selection for training with Noisy Labels","created":"2023-03-20","cited_by_count":1,"area":null},{"id":"2311.11473","title":"CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection","created":"2023-11-20","cited_by_count":1,"area":null},{"id":"2401.02150","title":"Marginal Debiased Network for Fair Visual Recognition","created":"2024-01-04","cited_by_count":1,"area":null},{"id":"2404.10499","title":"Robust Noisy Label Learning via Two-Stream Sample Distillation","created":"2024-04-16","cited_by_count":1,"area":null},{"id":"2407.17779","title":"DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction","created":"2024-07-25","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":"2410.14894","title":"Soft-Label Integration for Robust Toxicity Classification","created":"2024-10-18","cited_by_count":1,"area":null},{"id":"2501.10935","title":"TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval","created":"2025-01-19","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":"2502.09311","title":"Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection","created":"2025-02-13","cited_by_count":1,"area":null},{"id":"2502.14281","title":"Correcting Noisy Multilabel Predictions: Modeling Label Noise through Latent Space Shifts","created":"2025-02-20","cited_by_count":1,"area":null},{"id":"2512.03182","title":"Drainage: A Unifying Framework for Addressing Class Uncertainty","created":"2025-12-02","cited_by_count":1,"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":"2108.09154","title":"Contrastive Representations for Label Noise Require Fine-Tuning","created":"2021-08-20","cited_by_count":0,"area":null},{"id":"2109.04240","title":"MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces","created":"2021-09-09","cited_by_count":0,"area":null},{"id":"2206.02286","title":"AugLoss: A Robust Augmentation-based Fine Tuning Methodology","created":"2022-06-05","cited_by_count":0,"area":null},{"id":"2302.14695","title":"Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels","created":"2023-02-28","cited_by_count":0,"area":null},{"id":"2305.16823","title":"HUB: Guiding Learned Optimizers with Continuous Prompt Tuning","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2308.01609","title":"Feature Noise Boosts DNN Generalization under Label Noise","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2308.15132","title":"Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts","created":"2023-08-29","cited_by_count":0,"area":null},{"id":"2403.13480","title":"A Unified Optimal Transport Framework for Cross-Modal Retrieval with Noisy Labels","created":"2024-03-20","cited_by_count":0,"area":null},{"id":"2404.01853","title":"Pairwise Similarity Distribution Clustering for Noisy Label Learning","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2409.17474","title":"Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification","created":"2024-09-26","cited_by_count":0,"area":null},{"id":"2411.19581","title":"In-Context Learning with Noisy Labels","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2502.01474","title":"Simultaneous Automatic Picking and Manual Picking Refinement for First-Break","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2505.18741","title":"MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2506.03461","title":"RoNFA: Robust Neural Field-based Approach for Few-Shot Image Classification with Noisy Labels","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2508.06104","title":"MCA: 2D-3D Retrieval with Noisy Labels via Multi-level Adaptive Correction and Alignment","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2509.02351","title":"Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2511.10161","title":"DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning","created":"2025-11-13","cited_by_count":0,"area":null},{"id":"2511.18894","title":"Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels","created":"2025-11-24","cited_by_count":0,"area":null},{"id":"2601.17449","title":"DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2601.19947","title":"NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning","created":"2026-01-24","cited_by_count":0,"area":null}],"next_cursor":"100"}