{"total":1302,"rows":[{"id":"2110.08263","title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","created":"2021-10-15","cited_by_count":540,"area":null},{"id":"2106.01226","title":"Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision","created":"2021-06-02","cited_by_count":463,"area":null},{"id":"2303.15361","title":"A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts","created":"2023-03-27","cited_by_count":359,"area":null},{"id":"2106.09018","title":"End-to-End Semi-Supervised Object Detection with Soft Teacher","created":"2021-06-16","cited_by_count":316,"area":null},{"id":"2102.09480","title":"Unbiased Teacher for Semi-Supervised Object Detection","created":"2021-02-18","cited_by_count":306,"area":null},{"id":"2101.06329","title":"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning","created":"2021-01-15","cited_by_count":301,"area":null},{"id":"2104.06490","title":"DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort","created":"2021-04-13","cited_by_count":264,"area":null},{"id":"2110.12088","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","created":"2021-10-22","cited_by_count":256,"area":null},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251,"area":null},{"id":"2304.12210","title":"A Cookbook of Self-Supervised Learning","created":"2023-04-24","cited_by_count":250,"area":null},{"id":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232,"area":null},{"id":"2106.11037","title":"One Million Scenes for Autonomous Driving: ONCE Dataset","created":"2021-06-21","cited_by_count":218,"area":null},{"id":"2104.08826","title":"GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation","created":"2021-04-18","cited_by_count":187,"area":null},{"id":"2106.05095","title":"ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation","created":"2021-06-09","cited_by_count":184,"area":null},{"id":"2208.09910","title":"Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation","created":"2022-08-21","cited_by_count":178,"area":null},{"id":"2102.09559","title":"CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning","created":"2021-02-18","cited_by_count":175,"area":null},{"id":"2106.15590","title":"The Values Encoded in Machine Learning Research","created":"2021-06-29","cited_by_count":172,"area":null},{"id":"2102.03526","title":"Open-World Semi-Supervised Learning","created":"2021-02-06","cited_by_count":165,"area":null},{"id":"2202.03423","title":"Backdoor Defense via Decoupling the Training Process","created":"2022-02-05","cited_by_count":153,"area":null},{"id":"2105.00097","title":"Self-supervised Augmentation Consistency for Adapting Semantic Segmentation","created":"2021-04-30","cited_by_count":149,"area":null},{"id":"2109.00650","title":"Dash: Semi-Supervised Learning with Dynamic Thresholding","created":"2021-09-01","cited_by_count":148,"area":null},{"id":"2104.05833","title":"Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization","created":"2021-04-12","cited_by_count":141,"area":null},{"id":"2301.10921","title":"SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning","created":"2023-01-26","cited_by_count":133,"area":null},{"id":"2104.13415","title":"Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank","created":"2021-04-27","cited_by_count":125,"area":null},{"id":"2106.10731","title":"Neighborhood Contrastive Learning for Novel Class Discovery","created":"2021-06-20","cited_by_count":121,"area":null},{"id":"2103.03571","title":"Cycle Self-Training for Domain Adaptation","created":"2021-03-05","cited_by_count":120,"area":null},{"id":"2203.06915","title":"SimMatch: Semi-supervised Learning with Similarity Matching","created":"2022-03-14","cited_by_count":118,"area":null},{"id":"2101.08629","title":"Image-to-Image Translation: Methods and Applications","created":"2021-01-21","cited_by_count":116,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2104.13963","title":"Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples","created":"2021-04-28","cited_by_count":114,"area":null},{"id":"2103.11402","title":"Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework","created":"2021-03-21","cited_by_count":113,"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":"2111.12903","title":"Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation","created":"2021-11-25","cited_by_count":113,"area":null},{"id":"2211.11727","title":"Parametric Classification for Generalized Category Discovery: A Baseline Study","created":"2022-11-21","cited_by_count":113,"area":null},{"id":"2106.14133","title":"Semi-supervised Semantic Segmentation with Directional Context-aware Consistency","created":"2021-06-27","cited_by_count":112,"area":null},{"id":"2403.07366","title":"Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors","created":"2024-03-12","cited_by_count":106,"area":null},{"id":"2103.09382","title":"SPICE: Semantic Pseudo-labeling for Image Clustering","created":"2021-03-17","cited_by_count":104,"area":null},{"id":"2105.13381","title":"Recent advances and clinical applications of deep learning in medical image analysis","created":"2021-05-27","cited_by_count":104,"area":null},{"id":"2106.04732","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","created":"2021-06-08","cited_by_count":104,"area":null},{"id":"2302.11803","title":"A Comprehensive Survey on Source-free Domain Adaptation","created":"2023-02-23","cited_by_count":104,"area":null},{"id":"2106.07499","title":"An Empirical Survey of Data Augmentation for Limited Data Learning in NLP","created":"2021-06-14","cited_by_count":101,"area":null},{"id":"2110.05474","title":"Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning","created":"2021-10-11","cited_by_count":101,"area":null},{"id":"2104.07713","title":"Contrastive Learning with Stronger Augmentations","created":"2021-04-15","cited_by_count":100,"area":null},{"id":"2208.07204","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","created":"2022-08-12","cited_by_count":95,"area":null},{"id":"2105.13502","title":"Unsupervised Domain Adaptation of Object Detectors: A Survey","created":"2021-05-27","cited_by_count":93,"area":null},{"id":"2202.14026","title":"Robust Training under Label Noise by Over-parameterization","created":"2022-02-28","cited_by_count":93,"area":null},{"id":"2207.00026","title":"LaserMix for Semi-Supervised LiDAR Semantic Segmentation","created":"2022-06-30","cited_by_count":93,"area":null},{"id":"2303.17393","title":"Dynamic Conceptional Contrastive Learning for Generalized Category Discovery","created":"2023-03-30","cited_by_count":92,"area":null},{"id":"2104.00232","title":"Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition","created":"2021-04-01","cited_by_count":89,"area":null},{"id":"2103.02130","title":"Augmentation Strategies for Learning with Noisy Labels","created":"2021-03-03","cited_by_count":86,"area":null},{"id":"2110.07110","title":"Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast","created":"2021-10-14","cited_by_count":85,"area":null},{"id":"2201.01490","title":"Debiased Learning from Naturally Imbalanced Pseudo-Labels","created":"2022-01-05","cited_by_count":85,"area":null},{"id":"2103.16765","title":"Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation","created":"2021-03-31","cited_by_count":83,"area":null},{"id":"2102.11628","title":"FINE Samples for Learning with Noisy Labels","created":"2021-02-23","cited_by_count":82,"area":null},{"id":"2104.04465","title":"Bootstrapping Semantic Segmentation with Regional Contrast","created":"2021-04-09","cited_by_count":80,"area":null},{"id":"2105.08128","title":"PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training","created":"2021-05-17","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":"2104.07256","title":"A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation","created":"2021-04-15","cited_by_count":79,"area":null},{"id":"2107.11279","title":"Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation","created":"2021-07-23","cited_by_count":78,"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":"2308.11131","title":"ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","created":"2023-08-22","cited_by_count":77,"area":null},{"id":"2207.14191","title":"Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation","created":"2022-07-28","cited_by_count":76,"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":"2106.07760","title":"RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning","created":"2021-06-14","cited_by_count":74,"area":null},{"id":"2208.02812","title":"P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting","created":"2022-08-04","cited_by_count":74,"area":null},{"id":"2304.09355","title":"To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review","created":"2023-04-19","cited_by_count":72,"area":null},{"id":"2305.06144","title":"Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery","created":"2023-05-10","cited_by_count":72,"area":null},{"id":"2106.06600","title":"Break-It-Fix-It: Unsupervised Learning for Program Repair","created":"2021-06-11","cited_by_count":71,"area":null},{"id":"2411.18704","title":"Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits","created":"2024-11-27","cited_by_count":71,"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":"2103.15402","title":"Mining Latent Classes for Few-shot Segmentation","created":"2021-03-29","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":"2203.08537","title":"Scribble-Supervised LiDAR Semantic Segmentation","created":"2022-03-16","cited_by_count":69,"area":null},{"id":"2206.09500","title":"Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors","created":"2022-06-19","cited_by_count":69,"area":null},{"id":"2103.16725","title":"SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification","created":"2021-03-30","cited_by_count":68,"area":null},{"id":"2211.13222","title":"SVFormer: Semi-supervised Video Transformer for Action Recognition","created":"2022-11-23","cited_by_count":68,"area":null},{"id":"2104.09415","title":"Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation","created":"2021-04-19","cited_by_count":67,"area":null},{"id":"2202.02980","title":"3D Object Detection from Images for Autonomous Driving: A Survey","created":"2022-02-07","cited_by_count":66,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2110.10368","title":"ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning","created":"2021-10-20","cited_by_count":65,"area":null},{"id":"2207.10792","title":"Test-Time Adaptation via Self-Training with Nearest Neighbor Information","created":"2022-07-08","cited_by_count":64,"area":null},{"id":"2107.11355","title":"Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency","created":"2021-07-23","cited_by_count":63,"area":null},{"id":"2110.03740","title":"Adaptive Early-Learning Correction for Segmentation from Noisy Annotations","created":"2021-10-07","cited_by_count":62,"area":null},{"id":"2202.07136","title":"Debiased Self-Training for Semi-Supervised Learning","created":"2022-02-15","cited_by_count":62,"area":null},{"id":"2203.02261","title":"Class-Aware Contrastive Semi-Supervised Learning","created":"2022-03-04","cited_by_count":62,"area":null},{"id":"2110.08762","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","created":"2021-10-17","cited_by_count":61,"area":null},{"id":"2204.07683","title":"Safe Self-Refinement for Transformer-based Domain Adaptation","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2102.06289","title":"When and How Mixup Improves Calibration","created":"2021-02-11","cited_by_count":60,"area":null},{"id":"2409.07431","title":"Synthetic continued pretraining","created":"2024-09-11","cited_by_count":60,"area":null},{"id":"2110.06283","title":"Detecting Corrupted Labels Without Training a Model to Predict","created":"2021-10-12","cited_by_count":58,"area":null},{"id":"2104.11692","title":"A Closer Look at Self-training for Zero-Label Semantic Segmentation","created":"2021-04-21","cited_by_count":57,"area":null},{"id":"2103.01315","title":"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning","created":"2021-03-01","cited_by_count":56,"area":null},{"id":"2202.02200","title":"Learning with Neighbor Consistency for Noisy Labels","created":"2022-02-04","cited_by_count":55,"area":null},{"id":"2203.02533","title":"BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation","created":"2022-03-04","cited_by_count":54,"area":null},{"id":"2405.05258","title":"Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving","created":"2024-05-08","cited_by_count":54,"area":null},{"id":"2410.10777","title":"UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation","created":"2024-10-14","cited_by_count":54,"area":null},{"id":"2109.03856","title":"Local Augmentation for Graph Neural Networks","created":"2021-09-08","cited_by_count":53,"area":null},{"id":"2211.06612","title":"Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning","created":"2022-11-12","cited_by_count":53,"area":null},{"id":"2106.03408","title":"Antipodes of Label Differential Privacy: PATE and ALIBI","created":"2021-06-07","cited_by_count":52,"area":null},{"id":"2107.00085","title":"CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation","created":"2021-06-30","cited_by_count":52,"area":null}],"next_cursor":"100"}