{"total":137,"rows":[{"id":"2103.13630","title":"A Survey of Quantization Methods for Efficient Neural Network Inference","created":"2021-03-25","cited_by_count":815,"area":null},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802,"area":null},{"id":"2101.10979","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","created":"2021-01-26","cited_by_count":268,"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":"2103.16553","title":"Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers","created":"2021-03-30","cited_by_count":93,"area":null},{"id":"2108.06227","title":"SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation","created":"2021-08-13","cited_by_count":86,"area":null},{"id":"2110.06465","title":"Breaking the Dilemma of Medical Image-to-image Translation","created":"2021-10-13","cited_by_count":82,"area":null},{"id":"2103.15916","title":"Robust Audio-Visual Instance Discrimination","created":"2021-03-29","cited_by_count":80,"area":null},{"id":"2102.05262","title":"Input Similarity from the Neural Network Perspective","created":"2021-02-10","cited_by_count":79,"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":"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":"2207.10276","title":"ProMix: Combating Label Noise via Maximizing Clean Sample Utility","created":"2022-07-21","cited_by_count":48,"area":null},{"id":"2201.04309","title":"Robust Contrastive Learning against Noisy Views","created":"2022-01-12","cited_by_count":45,"area":null},{"id":"2103.15209","title":"Understanding the role of importance weighting for deep learning","created":"2021-03-28","cited_by_count":39,"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":"2112.09445","title":"Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation","created":"2021-12-17","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":"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":"2110.13041","title":"Applications and Techniques for Fast Machine Learning in Science","created":"2021-10-25","cited_by_count":32,"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":"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":"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":"2106.03300","title":"Sum of Ranked Range Loss for Supervised Learning","created":"2021-06-07","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":"2307.09933","title":"Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features","created":"2023-07-19","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":"2106.02654","title":"Churn Reduction via Distillation","created":"2021-06-04","cited_by_count":17,"area":null},{"id":"2207.13975","title":"On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification","created":"2022-07-28","cited_by_count":17,"area":null},{"id":"2208.05669","title":"PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation","created":"2022-08-11","cited_by_count":17,"area":null},{"id":"2104.02638","title":"Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark","created":"2021-04-06","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":"2206.01106","title":"Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space","created":"2022-06-02","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":"2111.05610","title":"CLIP2TV: Align, Match and Distill for Video-Text Retrieval","created":"2021-11-10","cited_by_count":14,"area":null},{"id":"2403.11395","title":"Automated data processing and feature engineering for deep learning and big data applications: a survey","created":"2024-03-18","cited_by_count":14,"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":"2203.04199","title":"Trustable Co-label Learning from Multiple Noisy Annotators","created":"2022-03-08","cited_by_count":12,"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":"2212.01674","title":"CrossSplit: Mitigating Label Noise Memorization through Data Splitting","created":"2022-12-03","cited_by_count":12,"area":null},{"id":"2106.06555","title":"Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network","created":"2021-06-11","cited_by_count":11,"area":null},{"id":"2307.15411","title":"Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning","created":"2023-07-28","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":"2302.09789","title":"Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement","created":"2023-02-20","cited_by_count":10,"area":null},{"id":"2105.03632","title":"CASIA-Face-Africa: A Large-scale African Face Image Database","created":"2021-05-08","cited_by_count":9,"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":"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":"2303.15833","title":"Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning","created":"2023-03-28","cited_by_count":9,"area":null},{"id":"2112.10453","title":"Learning with Label Noise for Image Retrieval by Selecting Interactions","created":"2021-12-20","cited_by_count":8,"area":null},{"id":"2204.09881","title":"CNLL: A Semi-supervised Approach For Continual Noisy Label Learning","created":"2022-04-21","cited_by_count":8,"area":null},{"id":"2306.01997","title":"UADB: Unsupervised Anomaly Detection Booster","created":"2023-06-03","cited_by_count":8,"area":null},{"id":"2308.10515","title":"QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection","created":"2023-08-21","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":"2403.13241","title":"Tackling Noisy Labels with Network Parameter Additive Decomposition","created":"2024-03-20","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":"2102.06406","title":"A Too-Good-to-be-True Prior to Reduce Shortcut Reliance","created":"2021-02-12","cited_by_count":7,"area":null},{"id":"2112.00725","title":"The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image","created":"2021-12-01","cited_by_count":7,"area":null},{"id":"2201.09196","title":"Learning to Predict Gradients for Semi-Supervised Continual Learning","created":"2022-01-23","cited_by_count":7,"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":"2303.01213","title":"DSD$^2$: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free?","created":"2023-03-02","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":"2109.05000","title":"Assessing the Quality of the Datasets by Identifying Mislabeled Samples","created":"2021-09-10","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":"2406.11206","title":"Retraining with Predicted Hard Labels Provably Increases Model Accuracy","created":"2024-06-17","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":"2211.14835","title":"CLID: Controlled-Length Image Descriptions with Limited Data","created":"2022-11-27","cited_by_count":4,"area":null},{"id":"2305.16337","title":"Handling Realistic Label Noise in BERT Text Classification","created":"2023-05-23","cited_by_count":4,"area":null},{"id":"2306.04502","title":"Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal","created":"2023-06-07","cited_by_count":4,"area":null},{"id":"2309.03331","title":"Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning","created":"2023-09-06","cited_by_count":4,"area":null},{"id":"2408.05503","title":"Disentangled Noisy Correspondence Learning","created":"2024-08-10","cited_by_count":4,"area":null},{"id":"2102.06735","title":"Learning Deep Neural Networks under Agnostic Corrupted Supervision","created":"2021-02-12","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":"2107.04497","title":"Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression","created":"2021-07-09","cited_by_count":3,"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":"2111.08165","title":"RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice","created":"2021-11-09","cited_by_count":3,"area":null},{"id":"2203.15529","title":"Treatment Learning Causal Transformer for Noisy Image Classification","created":"2022-03-29","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":"2404.06795","title":"Extracting Clean and Balanced Subset for Noisy Long-tailed Classification","created":"2024-04-10","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":"2103.11352","title":"Detecting Label Noise via Leave-One-Out Cross-Validation","created":"2021-03-21","cited_by_count":2,"area":null},{"id":"2103.17008","title":"Collaborative Label Correction via Entropy Thresholding","created":"2021-03-31","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":"2107.11061","title":"Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition","created":"2021-07-23","cited_by_count":2,"area":null},{"id":"2111.12963","title":"Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU Neural Networks","created":"2021-11-25","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":"2208.10683","title":"Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling","created":"2022-08-23","cited_by_count":2,"area":null},{"id":"2210.05126","title":"Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration","created":"2022-10-11","cited_by_count":2,"area":null},{"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":"2306.11911","title":"LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2402.18495","title":"ROG$_{PL}$: Robust Open-Set Graph Learning via Region-Based Prototype Learning","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2405.07083","title":"Data-Efficient and Robust Task Selection for Meta-Learning","created":"2024-05-11","cited_by_count":2,"area":null},{"id":"2409.03254","title":"Granular-ball Representation Learning for Deep CNN on Learning with Label Noise","created":"2024-09-05","cited_by_count":2,"area":null},{"id":"2411.09776","title":"Combining Machine Learning Defenses without Conflicts","created":"2024-11-14","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":"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":"2108.05092","title":"Cooperative Learning for Noisy Supervision","created":"2021-08-11","cited_by_count":1,"area":null}],"next_cursor":"100"}