{"total":420,"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":"2106.09018","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":"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":"2203.03884","title":"Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels","created":"2022-03-08","cited_by_count":218,"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":"2212.01322","title":"MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation","created":"2022-12-02","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":"2204.13132","title":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation","created":"2022-04-27","cited_by_count":164,"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":"2103.03571","title":"Cycle Self-Training for Domain Adaptation","created":"2021-03-05","cited_by_count":120,"area":null},{"id":"2111.12525","title":"Causality-inspired Single-source Domain Generalization for Medical Image Segmentation","created":"2021-11-24","cited_by_count":119,"area":null},{"id":"2203.06915","title":"SimMatch: Semi-supervised Learning with Similarity Matching","created":"2022-03-14","cited_by_count":118,"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":"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":"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":"2105.08128","title":"PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training","created":"2021-05-17","cited_by_count":80,"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":"2206.09500","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":"2205.00049","title":"Prompt Consistency for Zero-Shot Task Generalization","created":"2022-04-29","cited_by_count":67,"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":"2105.14859","title":"Consistency Regularization for Variational Auto-Encoders","created":"2021-05-31","cited_by_count":51,"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":"2206.06608","title":"Label Matching Semi-Supervised Object Detection","created":"2022-06-14","cited_by_count":44,"area":null},{"id":"2306.04300","title":"CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation","created":"2023-06-07","cited_by_count":44,"area":null},{"id":"2105.14148","title":"OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers","created":"2021-05-28","cited_by_count":43,"area":null},{"id":"2106.11118","title":"SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving","created":"2021-06-21","cited_by_count":43,"area":null},{"id":"2112.09645","title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation","created":"2021-12-17","cited_by_count":43,"area":null},{"id":"2302.01735","title":"Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective","created":"2023-02-03","cited_by_count":43,"area":null},{"id":"2112.04564","title":"CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning","created":"2021-12-08","cited_by_count":41,"area":null},{"id":"2102.03897","title":"Self-supervised driven consistency training for annotation efficient histopathology image analysis","created":"2021-02-07","cited_by_count":39,"area":null},{"id":"2207.02261","title":"OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning","created":"2022-07-05","cited_by_count":37,"area":null},{"id":"2103.13041","title":"Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization","created":"2021-03-24","cited_by_count":36,"area":null},{"id":"2104.00679","title":"A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification","created":"2021-04-01","cited_by_count":36,"area":null},{"id":"2101.06480","title":"SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning","created":"2021-01-16","cited_by_count":35,"area":null},{"id":"2112.05825","title":"Revisiting Consistency Regularization for Semi-Supervised Learning","created":"2021-12-10","cited_by_count":34,"area":null},{"id":"2208.02764","title":"OpenCon: Open-world Contrastive Learning","created":"2022-08-04","cited_by_count":33,"area":null},{"id":"2303.11203","title":"Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation","created":"2023-03-20","cited_by_count":33,"area":null},{"id":"2102.08622","title":"Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training","created":"2021-02-17","cited_by_count":32,"area":null},{"id":"2103.02093","title":"Pseudo-labeling for Scalable 3D Object Detection","created":"2021-03-02","cited_by_count":32,"area":null},{"id":"2104.00682","title":"Multiview Pseudo-Labeling for Semi-supervised Learning from Video","created":"2021-04-01","cited_by_count":32,"area":null},{"id":"2104.05248","title":"All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training","created":"2021-04-12","cited_by_count":32,"area":null},{"id":"2203.12341","title":"Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margin","created":"2022-03-23","cited_by_count":32,"area":null},{"id":"2210.01883","title":"Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions","created":"2022-10-04","cited_by_count":32,"area":null},{"id":"2210.05174","title":"BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation","created":"2022-10-11","cited_by_count":31,"area":null},{"id":"2209.13476","title":"Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels","created":"2022-09-27","cited_by_count":30,"area":null},{"id":"2308.12595","title":"Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation","created":"2023-08-24","cited_by_count":29,"area":null},{"id":"2107.05031","title":"Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training","created":"2021-07-11","cited_by_count":28,"area":null},{"id":"2110.06282","title":"The Rich Get Richer: Disparate Impact of Semi-Supervised Learning","created":"2021-10-12","cited_by_count":28,"area":null},{"id":"2110.10232","title":"Test time Adaptation through Perturbation Robustness","created":"2021-10-19","cited_by_count":28,"area":null},{"id":"2201.09381","title":"vCLIMB: A Novel Video Class Incremental Learning Benchmark","created":"2022-01-23","cited_by_count":28,"area":null},{"id":"2202.12230","title":"Sample Efficiency of Data Augmentation Consistency Regularization","created":"2022-02-24","cited_by_count":28,"area":null},{"id":"2106.11575","title":"Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering","created":"2021-06-22","cited_by_count":27,"area":null},{"id":"2110.02470","title":"SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision","created":"2021-10-06","cited_by_count":27,"area":null},{"id":"2110.03006","title":"Unsupervised Selective Labeling for More Effective Semi-Supervised Learning","created":"2021-10-06","cited_by_count":27,"area":null},{"id":"2207.02269","title":"Towards Realistic Semi-Supervised Learning","created":"2022-07-05","cited_by_count":27,"area":null},{"id":"2105.07387","title":"Semi-supervised Contrastive Learning with Similarity Co-calibration","created":"2021-05-16","cited_by_count":26,"area":null},{"id":"2108.06552","title":"Continual Semi-Supervised Learning through Contrastive Interpolation Consistency","created":"2021-08-14","cited_by_count":25,"area":null},{"id":"2207.01066","title":"NP-Match: When Neural Processes meet Semi-Supervised Learning","created":"2022-07-03","cited_by_count":25,"area":null},{"id":"2208.08631","title":"ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization","created":"2022-08-18","cited_by_count":25,"area":null},{"id":"2303.09373","title":"MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling","created":"2023-03-16","cited_by_count":25,"area":null},{"id":"2201.06247","title":"Contrastive Regularization for Semi-Supervised Learning","created":"2022-01-17","cited_by_count":24,"area":null},{"id":"2203.16219","title":"Acknowledging the Unknown for Multi-label Learning with Single Positive Labels","created":"2022-03-30","cited_by_count":24,"area":null},{"id":"2202.11915","title":"Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning","created":"2022-02-24","cited_by_count":23,"area":null},{"id":"2204.05859","title":"Bootstrap Motion Forecasting With Self-Consistent Constraints","created":"2022-04-12","cited_by_count":23,"area":null},{"id":"2205.04980","title":"ALLSH: Active Learning Guided by Local Sensitivity and Hardness","created":"2022-05-10","cited_by_count":23,"area":null},{"id":"2211.03154","title":"On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey","created":"2022-11-06","cited_by_count":23,"area":null},{"id":"2301.02830","title":"Image Data Augmentation Approaches: A Comprehensive Survey and Future directions","created":"2023-01-07","cited_by_count":23,"area":null},{"id":"2308.13168","title":"IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization","created":"2023-08-25","cited_by_count":23,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2103.16565","title":"Learning Representational Invariances for Data-Efficient Action Recognition","created":"2021-03-30","cited_by_count":22,"area":null},{"id":"2107.08943","title":"OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data","created":"2021-06-29","cited_by_count":22,"area":null},{"id":"2311.03524","title":"A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning","created":"2023-11-06","cited_by_count":22,"area":null},{"id":"2110.08552","title":"Virtual Augmentation Supported Contrastive Learning of Sentence Representations","created":"2021-10-16","cited_by_count":21,"area":null},{"id":"2111.02682","title":"TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation","created":"2021-11-04","cited_by_count":21,"area":null},{"id":"2108.03429","title":"Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation","created":"2021-08-07","cited_by_count":20,"area":null},{"id":"2108.12296","title":"Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain","created":"2021-08-27","cited_by_count":20,"area":null},{"id":"2109.12265","title":"Label-Assemble: Leveraging Multiple Datasets with Partial Labels","created":"2021-09-25","cited_by_count":20,"area":null},{"id":"2203.09510","title":"DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection","created":"2022-03-17","cited_by_count":20,"area":null},{"id":"2306.07483","title":"Semi-supervised learning made simple with self-supervised clustering","created":"2023-06-13","cited_by_count":20,"area":null},{"id":"2101.12727","title":"Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency","created":"2021-01-29","cited_by_count":19,"area":null},{"id":"2201.08514","title":"How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis","created":"2022-01-21","cited_by_count":19,"area":null},{"id":"2304.04515","title":"SOOD: Towards Semi-Supervised Oriented Object Detection","created":"2023-04-10","cited_by_count":19,"area":null},{"id":"2405.11756","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","created":"2024-05-20","cited_by_count":19,"area":null},{"id":"2110.13740","title":"DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples","created":"2021-10-26","cited_by_count":18,"area":null},{"id":"2207.13856","title":"Imbalanced Semi-supervised Learning with Bias Adaptive Classifier","created":"2022-07-28","cited_by_count":18,"area":null},{"id":"2208.08605","title":"Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures","created":"2022-08-18","cited_by_count":18,"area":null}],"next_cursor":"100"}