{"total":78,"rows":[{"id":"2106.07411","title":"Partial success in closing the gap between human and machine vision","created":"2021-06-14","cited_by_count":185,"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":"2105.04522","title":"Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels","created":"2021-05-10","cited_by_count":74,"area":null},{"id":"2206.03014","title":"The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation","created":"2022-06-07","cited_by_count":60,"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":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2502.01968","title":"Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning","created":"2025-02-04","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":"2104.06574","title":"Joint Negative and Positive Learning for Noisy Labels","created":"2021-04-14","cited_by_count":34,"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":"2103.04618","title":"Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification","created":"2021-03-08","cited_by_count":29,"area":null},{"id":"2112.07431","title":"Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation","created":"2021-12-14","cited_by_count":29,"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":"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":"2103.03414","title":"Unified Robust Training for Graph NeuralNetworks against Label Noise","created":"2021-03-05","cited_by_count":21,"area":null},{"id":"2207.13316","title":"NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation","created":"2022-07-27","cited_by_count":19,"area":null},{"id":"2308.12064","title":"SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels","created":"2023-08-23","cited_by_count":19,"area":null},{"id":"2110.01242","title":"Consistency Regularization Can Improve Robustness to Label Noise","created":"2021-10-04","cited_by_count":18,"area":null},{"id":"2104.01493","title":"Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond","created":"2021-04-03","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":"2111.00056","title":"Generalized Data Weighting via Class-level Gradient Manipulation","created":"2021-10-29","cited_by_count":15,"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":"2203.13704","title":"Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos","created":"2022-03-25","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":"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":"2202.04291","title":"L2B: Learning to Bootstrap Robust Models for Combating Label Noise","created":"2022-02-09","cited_by_count":10,"area":null},{"id":"2105.07338","title":"CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise","created":"2021-05-16","cited_by_count":9,"area":null},{"id":"2110.10456","title":"Noisy Annotation Refinement for Object Detection","created":"2021-10-20","cited_by_count":9,"area":null},{"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":"2302.09595","title":"Latent Class-Conditional Noise Model","created":"2023-02-19","cited_by_count":9,"area":null},{"id":"2407.02778","title":"Foster Adaptivity and Balance in Learning with Noisy Labels","created":"2024-07-03","cited_by_count":9,"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":"2403.13241","title":"Tackling Noisy Labels with Network Parameter Additive Decomposition","created":"2024-03-20","cited_by_count":8,"area":null},{"id":"2104.02570","title":"Learning from Noisy Labels via Dynamic Loss Thresholding","created":"2021-04-01","cited_by_count":6,"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":"2206.05708","title":"Narrowing the Gap: Improved Detector Training with Noisy Location Annotations","created":"2022-06-12","cited_by_count":6,"area":null},{"id":"2304.08275","title":"Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects","created":"2023-04-17","cited_by_count":6,"area":null},{"id":"2108.08212","title":"Confidence Adaptive Regularization for Deep Learning with Noisy Labels","created":"2021-08-18","cited_by_count":5,"area":null},{"id":"2204.08954","title":"Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity","created":"2022-04-19","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":"2102.12192","title":"Multiplicative Reweighting for Robust Neural Network Optimization","created":"2021-02-24","cited_by_count":4,"area":null},{"id":"2210.03339","title":"Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification","created":"2022-10-07","cited_by_count":4,"area":null},{"id":"2306.07621","title":"Rank-Aware Negative Training for Semi-Supervised Text Classification","created":"2023-06-13","cited_by_count":3,"area":null},{"id":"2412.13516","title":"Learning Causal Transition Matrix for Instance-dependent Label Noise","created":"2024-12-18","cited_by_count":3,"area":null},{"id":"2103.10824","title":"Enhancing Robustness of On-line Learning Models on Highly Noisy Data","created":"2021-03-19","cited_by_count":2,"area":null},{"id":"2105.13892","title":"Training Classifiers that are Universally Robust to All Label Noise Levels","created":"2021-05-27","cited_by_count":2,"area":null},{"id":"2110.06827","title":"NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels","created":"2021-10-13","cited_by_count":2,"area":null},{"id":"2110.12197","title":"Towards a Robust Differentiable Architecture Search under Label Noise","created":"2021-10-23","cited_by_count":2,"area":null},{"id":"2201.12739","title":"Do We Need to Penalize Variance of Losses for Learning with Label Noise?","created":"2022-01-30","cited_by_count":2,"area":null},{"id":"2206.06177","title":"Transductive CLIP with Class-Conditional Contrastive Learning","created":"2022-06-13","cited_by_count":2,"area":"mllm"},{"id":"2211.16756","title":"Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning","created":"2022-11-30","cited_by_count":2,"area":null},{"id":"2307.06542","title":"Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2103.04685","title":"A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels","created":"2021-03-08","cited_by_count":1,"area":null},{"id":"2103.07808","title":"Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification","created":"2021-03-13","cited_by_count":1,"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":"2104.00319","title":"Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training","created":"2021-04-01","cited_by_count":1,"area":null},{"id":"2107.02347","title":"An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels","created":"2021-07-06","cited_by_count":1,"area":null},{"id":"2202.08436","title":"PENCIL: Deep Learning with Noisy Labels","created":"2022-02-17","cited_by_count":1,"area":null},{"id":"2206.11851","title":"Context-based Virtual Adversarial Training for Text Classification with Noisy Labels","created":"2022-05-29","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":"2308.05101","title":"DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels","created":"2023-08-09","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":"2503.02897","title":"ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection","created":"2025-03-03","cited_by_count":1,"area":"mllm"},{"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":"2105.05052","title":"Joint Text and Label Generation for Spoken Language Understanding","created":"2021-05-11","cited_by_count":0,"area":null},{"id":"2107.07958","title":"Temporal-aware Language Representation Learning From Crowdsourced Labels","created":"2021-07-15","cited_by_count":0,"area":null},{"id":"2108.00784","title":"Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling","created":"2021-08-02","cited_by_count":0,"area":null},{"id":"2201.10212","title":"Feature Diversity Learning with Sample Dropout for Unsupervised Domain Adaptive Person Re-identification","created":"2022-01-25","cited_by_count":0,"area":null},{"id":"2203.15317","title":"Agreement or Disagreement in Noise-tolerant Mutual Learning?","created":"2022-03-29","cited_by_count":0,"area":null},{"id":"2206.11988","title":"On making optimal transport robust to all outliers","created":"2022-06-23","cited_by_count":0,"area":null},{"id":"2211.04177","title":"Learning advisor networks for noisy image classification","created":"2022-11-08","cited_by_count":0,"area":null},{"id":"2212.10766","title":"Class Prototype-based Cleaner for Label Noise Learning","created":"2022-12-21","cited_by_count":0,"area":null},{"id":"2301.05858","title":"Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking","created":"2023-01-14","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},{"id":"2501.15163","title":"The Exploration of Error Bounds in Classification with Noisy Labels","created":"2025-01-25","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":"2603.17255","title":"Variational Rectification Inference for Learning with Noisy Labels","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2607.16768","title":"Robust Losses from Univariate Base Functions for Noisy-Label Learning","created":"2026-07-18","cited_by_count":0,"area":null}],"next_cursor":null}