{"total":975,"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":"2102.08604","title":"SWAD: Domain Generalization by Seeking Flat Minima","created":"2021-02-17","cited_by_count":434,"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":"2105.11120","title":"A Fourier-based Framework for Domain Generalization","created":"2021-05-24","cited_by_count":290,"area":null},{"id":"2104.02395","title":"Ensemble deep learning: A review","created":"2021-04-06","cited_by_count":284,"area":null},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251,"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":"2201.02609","title":"Generalized Category Discovery","created":"2022-01-07","cited_by_count":211,"area":null},{"id":"2202.04538","title":"Generating Training Data with Language Models: Towards Zero-Shot Language Understanding","created":"2022-02-09","cited_by_count":199,"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":"2102.03526","title":"Open-World Semi-Supervised Learning","created":"2021-02-06","cited_by_count":165,"area":null},{"id":"2110.03374","title":"Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data","created":"2021-10-07","cited_by_count":152,"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":"2203.16258","title":"Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data","created":"2022-03-30","cited_by_count":126,"area":null},{"id":"2203.06915","title":"SimMatch: Semi-supervised Learning with Similarity Matching","created":"2022-03-14","cited_by_count":118,"area":null},{"id":"2106.15252","title":"AutoNovel: Automatically Discovering and Learning Novel Visual Categories","created":"2021-06-29","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":"2111.12903","title":"Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation","created":"2021-11-25","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":"2109.09960","title":"Mutual Consistency Learning for Semi-supervised Medical Image Segmentation","created":"2021-09-21","cited_by_count":108,"area":null},{"id":"2104.03310","title":"Regularizing Generative Adversarial Networks under Limited Data","created":"2021-04-07","cited_by_count":107,"area":null},{"id":"2103.02911","title":"Semi-supervised Left Atrium Segmentation with Mutual Consistency Training","created":"2021-03-04","cited_by_count":104,"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":"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":"2103.16397","title":"3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding","created":"2021-03-30","cited_by_count":100,"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":"2203.01324","title":"Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation","created":"2022-03-02","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":"2309.15698","title":"Deep Model Fusion: A Survey","created":"2023-09-27","cited_by_count":91,"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":"2104.02246","title":"One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation","created":"2021-04-06","cited_by_count":89,"area":null},{"id":"2110.08188","title":"Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation","created":"2021-10-15","cited_by_count":87,"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":"2202.12040","title":"Self-Training: A Survey","created":"2022-02-24","cited_by_count":86,"area":null},{"id":"2103.13258","title":"Dynamic Slimmable Network","created":"2021-03-24","cited_by_count":83,"area":null},{"id":"2301.00265","title":"Source-Free Unsupervised Domain Adaptation: A Survey","created":"2022-12-31","cited_by_count":80,"area":null},{"id":"2101.08482","title":"Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning","created":"2021-01-21","cited_by_count":78,"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":"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":"2206.02749","title":"CORE: Consistent Representation Learning for Face Forgery Detection","created":"2022-06-06","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":"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":"2103.16364","title":"ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification","created":"2021-03-30","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":"2101.00407","title":"ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning","created":"2021-01-02","cited_by_count":68,"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":"2108.02934","title":"From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data","created":"2021-08-06","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":"2106.05731","title":"Leveraged Weighted Loss for Partial Label Learning","created":"2021-06-10","cited_by_count":66,"area":null},{"id":"2104.04891","title":"SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds","created":"2021-04-11","cited_by_count":64,"area":null},{"id":"2203.01475","title":"CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision","created":"2022-03-03","cited_by_count":64,"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":"2212.04976","title":"Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation","created":"2022-12-09","cited_by_count":64,"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":"2110.12024","title":"A Prototype-Oriented Framework for Unsupervised Domain Adaptation","created":"2021-10-22","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":"2110.08762","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","created":"2021-10-17","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":"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":"2104.12673","title":"Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data","created":"2021-04-26","cited_by_count":55,"area":null},{"id":"2106.01606","title":"Exploring Memorization in Adversarial Training","created":"2021-06-03","cited_by_count":55,"area":null},{"id":"2201.09637","title":"DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations","created":"2022-01-24","cited_by_count":54,"area":null},{"id":"2210.02984","title":"The Lie Derivative for Measuring Learned Equivariance","created":"2022-10-06","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":"2109.03856","title":"Local Augmentation for Graph Neural Networks","created":"2021-09-08","cited_by_count":53,"area":null},{"id":"2208.11296","title":"Semi-Supervised and Unsupervised Deep Visual Learning: A Survey","created":"2022-08-24","cited_by_count":52,"area":null},{"id":"2104.01539","title":"DINE: Domain Adaptation from Single and Multiple Black-box Predictors","created":"2021-04-04","cited_by_count":51,"area":null},{"id":"2105.14859","title":"Consistency Regularization for Variational Auto-Encoders","created":"2021-05-31","cited_by_count":51,"area":null},{"id":"2106.05682","title":"DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning","created":"2021-06-10","cited_by_count":51,"area":null},{"id":"2102.02751","title":"Semi-Supervised Action Recognition with Temporal Contrastive Learning","created":"2021-02-04","cited_by_count":50,"area":null},{"id":"2107.14153","title":"Semi-Supervised Active Learning with Temporal Output Discrepancy","created":"2021-07-29","cited_by_count":50,"area":null},{"id":"2210.04388","title":"Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization","created":"2022-10-10","cited_by_count":50,"area":null},{"id":"2103.16368","title":"Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection","created":"2021-03-29","cited_by_count":49,"area":null},{"id":"2105.07059","title":"Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation","created":"2021-05-14","cited_by_count":49,"area":null},{"id":"2208.05688","title":"Semi-supervised Vision Transformers at Scale","created":"2022-08-11","cited_by_count":49,"area":null},{"id":"2303.00918","title":"STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables","created":"2023-03-02","cited_by_count":49,"area":null},{"id":"2207.02541","title":"Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection","created":"2022-07-06","cited_by_count":48,"area":null},{"id":"2101.09387","title":"Online Adversarial Purification based on Self-Supervision","created":"2021-01-23","cited_by_count":46,"area":null},{"id":"2106.00168","title":"Rethinking Pseudo Labels for Semi-Supervised Object Detection","created":"2021-06-01","cited_by_count":45,"area":null},{"id":"2106.06133","title":"Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification","created":"2021-06-11","cited_by_count":45,"area":null},{"id":"2110.10211","title":"Learning Partial Equivariances from Data","created":"2021-10-19","cited_by_count":45,"area":null},{"id":"2205.10334","title":"UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation","created":"2022-05-20","cited_by_count":45,"area":null},{"id":"2207.12655","title":"Semi-supervised 3D Object Detection with Proficient Teachers","created":"2022-07-26","cited_by_count":45,"area":null},{"id":"2105.01622","title":"Poisoning the Unlabeled Dataset of Semi-Supervised Learning","created":"2021-05-04","cited_by_count":44,"area":null}],"next_cursor":"100"}