{"total":25,"rows":[{"id":"2404.17399","title":"Evaluations of Machine Learning Privacy Defenses are Misleading","created":"2024-04-26","cited_by_count":36,"area":null},{"id":"2405.03676","title":"Why is SAM Robust to Label Noise?","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2205.12702","title":"Detecting Label Errors by using Pre-Trained Language Models","created":"2022-05-25","cited_by_count":16,"area":null},{"id":"2210.00094","title":"Improving Robustness with Adaptive Weight Decay","created":"2022-09-30","cited_by_count":13,"area":null},{"id":"2402.13635","title":"The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review","created":"2024-02-21","cited_by_count":13,"area":null},{"id":"2210.03920","title":"Detecting Label Errors in Token Classification Data","created":"2022-10-08","cited_by_count":12,"area":null},{"id":"2211.13895","title":"Identifying Incorrect Annotations in Multi-Label Classification Data","created":"2022-11-25","cited_by_count":12,"area":null},{"id":"2307.05080","title":"Estimating label quality and errors in semantic segmentation data via any model","created":"2023-07-11","cited_by_count":12,"area":null},{"id":"2309.00832","title":"ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data","created":"2023-09-02","cited_by_count":11,"area":null},{"id":"2405.03869","title":"Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models","created":"2024-05-06","cited_by_count":10,"area":null},{"id":"2207.00694","title":"Efficient Adversarial Training With Data Pruning","created":"2022-07-01","cited_by_count":7,"area":null},{"id":"2403.10820","title":"Active Label Correction for Semantic Segmentation with Foundation Models","created":"2024-03-16","cited_by_count":7,"area":null},{"id":"2210.11513","title":"Learning Sample Reweighting for Accuracy and Adversarial Robustness","created":"2022-10-20","cited_by_count":6,"area":null},{"id":"2109.05000","title":"Assessing the Quality of the Datasets by Identifying Mislabeled Samples","created":"2021-09-10","cited_by_count":5,"area":null},{"id":"2302.06598","title":"Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning","created":"2023-02-13","cited_by_count":5,"area":null},{"id":"2305.11957","title":"Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method","created":"2023-05-19","cited_by_count":5,"area":null},{"id":"2306.12550","title":"On Evaluation of Document Classification using RVL-CDIP","created":"2023-06-21","cited_by_count":5,"area":null},{"id":"2109.09191","title":"Training Dynamic based data filtering may not work for NLP datasets","created":"2021-09-19","cited_by_count":3,"area":null},{"id":"2503.03236","title":"GenColor: Generative Color-Concept Association in Visual Design","created":"2025-03-05","cited_by_count":3,"area":null},{"id":"2209.15380","title":"Identify ambiguous tasks combining crowdsourced labels by weighting Areas Under the Margin","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2305.16583","title":"Detecting Errors in a Numerical Response via any Regression Model","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2508.17930","title":"Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2605.18648","title":"An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.10666","title":"Analyzing Training-Free Corruption Detection for Object Detection Datasets","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2606.31446","title":"Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap","created":"2026-06-30","cited_by_count":0,"area":null}],"next_cursor":null}