{"total":315,"rows":[{"id":"2110.08420","title":"Understanding Dataset Difficulty with $\\mathcal{V}$-Usable Information","created":"2021-10-16","cited_by_count":291,"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":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86,"area":null},{"id":"2111.12698","title":"Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling","created":"2021-11-24","cited_by_count":84,"area":null},{"id":"2306.02050","title":"Provable Dynamic Fusion for Low-Quality Multimodal Data","created":"2023-06-03","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":"2301.00265","title":"Source-Free Unsupervised Domain Adaptation: A Survey","created":"2022-12-31","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":"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":"2204.04677","title":"FedCorr: Multi-Stage Federated Learning for Label Noise Correction","created":"2022-04-10","cited_by_count":61,"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":"2303.03108","title":"Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization","created":"2023-03-03","cited_by_count":57,"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":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","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":"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":"2207.00810","title":"Eliciting and Learning with Soft Labels from Every Annotator","created":"2022-07-02","cited_by_count":43,"area":null},{"id":"2403.01931","title":"VariErr NLI: Separating Annotation Error from Human Label Variation","created":"2024-03-04","cited_by_count":42,"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":"2308.06879","title":"Towards Open-Set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization","created":"2023-08-14","cited_by_count":39,"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":"2206.08063","title":"Towards Robust Ranker for Text Retrieval","created":"2022-06-16","cited_by_count":37,"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":"2104.06574","title":"Joint Negative and Positive Learning for Noisy Labels","created":"2021-04-14","cited_by_count":34,"area":null},{"id":"2109.06415","title":"Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction","created":"2021-09-14","cited_by_count":33,"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":"2203.15355","title":"Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries","created":"2022-03-29","cited_by_count":31,"area":null},{"id":"2112.07368","title":"Simple and Robust Loss Design for Multi-Label Learning with Missing Labels","created":"2021-12-13","cited_by_count":30,"area":null},{"id":"2207.03036","title":"Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space","created":"2022-07-07","cited_by_count":30,"area":null},{"id":"2204.11573","title":"Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing","created":"2022-04-25","cited_by_count":29,"area":null},{"id":"2104.04318","title":"Noisy-Labeled NER with Confidence Estimation","created":"2021-04-09","cited_by_count":28,"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":"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":"2305.14910","title":"From Shortcuts to Triggers: Backdoor Defense with Denoised PoE","created":"2023-05-24","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":"2304.06275","title":"Noisy Correspondence Learning with Meta Similarity Correction","created":"2023-04-13","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":"2107.14230","title":"Learning with Noisy Labels for Robust Point Cloud Segmentation","created":"2021-07-29","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":"2304.01184","title":"WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation","created":"2023-04-03","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":"2106.15292","title":"Adaptive Sample Selection for Robust Learning under Label Noise","created":"2021-06-29","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":"2206.07551","title":"Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation","created":"2022-06-15","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":"2405.03676","title":"Why is SAM Robust to Label Noise?","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2204.02620","title":"Towards Robust Adaptive Object Detection under Noisy Annotations","created":"2022-04-06","cited_by_count":20,"area":null},{"id":"2212.07086","title":"NLIP: Noise-robust Language-Image Pre-training","created":"2022-12-14","cited_by_count":20,"area":null},{"id":"2110.07647","title":"Towards Understanding the Data Dependency of Mixup-style Training","created":"2021-10-14","cited_by_count":19,"area":null},{"id":"2205.14467","title":"Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictors","created":"2022-05-28","cited_by_count":19,"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":"2112.02960","title":"Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise","created":"2021-12-06","cited_by_count":18,"area":null},{"id":"2604.19386","title":"Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval","created":"2026-04-21","cited_by_count":18,"area":null},{"id":"2108.11096","title":"Learning From Long-Tailed Data With Noisy Labels","created":"2021-08-25","cited_by_count":17,"area":null},{"id":"2110.11809","title":"PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels","created":"2021-10-22","cited_by_count":17,"area":null},{"id":"2208.08464","title":"CTRL: Clustering Training Losses for Label Error Detection","created":"2022-08-17","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":"2312.06221","title":"CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels","created":"2023-12-11","cited_by_count":17,"area":null},{"id":"2103.14242","title":"Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation","created":"2021-03-26","cited_by_count":16,"area":null},{"id":"2305.15817","title":"Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term","created":"2023-05-25","cited_by_count":16,"area":null},{"id":"2305.18377","title":"BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning","created":"2023-05-28","cited_by_count":16,"area":null},{"id":"2408.10012","title":"CLIPCleaner: Cleaning Noisy Labels with CLIP","created":"2024-08-19","cited_by_count":16,"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":"2110.13699","title":"Addressing out-of-distribution label noise in webly-labelled data","created":"2021-10-26","cited_by_count":14,"area":null},{"id":"2111.05610","title":"CLIP2TV: Align, Match and Distill for Video-Text Retrieval","created":"2021-11-10","cited_by_count":14,"area":null},{"id":"2210.13512","title":"Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup","created":"2022-10-24","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":"2103.11395","title":"ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning","created":"2021-03-21","cited_by_count":13,"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":"2208.09378","title":"Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels","created":"2022-08-19","cited_by_count":13,"area":null},{"id":"2307.11618","title":"Divide and Adapt: Active Domain Adaptation via Customized Learning","created":"2023-07-21","cited_by_count":13,"area":null},{"id":"2312.12263","title":"FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels","created":"2023-12-19","cited_by_count":13,"area":null},{"id":"2106.04060","title":"Self-supervised and Supervised Joint Training for Resource-rich Machine Translation","created":"2021-06-08","cited_by_count":12,"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":"2111.05428","title":"Constrained Instance and Class Reweighting for Robust Learning under Label Noise","created":"2021-11-09","cited_by_count":12,"area":null},{"id":"2205.14361","title":"Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2212.01674","title":"CrossSplit: Mitigating Label Noise Memorization through Data Splitting","created":"2022-12-03","cited_by_count":12,"area":null},{"id":"2103.12002","title":"On the Robustness of Monte Carlo Dropout Trained with Noisy Labels","created":"2021-03-22","cited_by_count":11,"area":null},{"id":"2210.04578","title":"Is your noise correction noisy? PLS: Robustness to label noise with two stage detection","created":"2022-10-10","cited_by_count":11,"area":null},{"id":"2210.06788","title":"TiDAL: Learning Training Dynamics for Active Learning","created":"2022-10-13","cited_by_count":11,"area":null},{"id":"2212.06641","title":"Simplicity Bias Leads to Amplified Performance Disparities","created":"2022-12-13","cited_by_count":11,"area":null},{"id":"2303.10365","title":"CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning","created":"2023-03-18","cited_by_count":11,"area":null},{"id":"2306.11650","title":"FedNoisy: Federated Noisy Label Learning Benchmark","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2403.05105","title":"Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval","created":"2024-03-08","cited_by_count":11,"area":null},{"id":"2408.00298","title":"Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names","created":"2024-08-01","cited_by_count":11,"area":null},{"id":"2105.00349","title":"Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise","created":"2021-05-01","cited_by_count":10,"area":null},{"id":"2106.11566","title":"SENT: Sentence-level Distant Relation Extraction via Negative Training","created":"2021-06-22","cited_by_count":10,"area":null}],"next_cursor":"100"}