{"total":227,"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":"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":"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":"2103.03571","title":"Cycle Self-Training for Domain Adaptation","created":"2021-03-05","cited_by_count":120,"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":"2106.07499","title":"An Empirical Survey of Data Augmentation for Limited Data Learning in NLP","created":"2021-06-14","cited_by_count":101,"area":null},{"id":"2205.00049","title":"Prompt Consistency for Zero-Shot Task Generalization","created":"2022-04-29","cited_by_count":67,"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":"2106.03408","title":"Antipodes of Label Differential Privacy: PATE and ALIBI","created":"2021-06-07","cited_by_count":52,"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":"2102.02751","title":"Semi-Supervised Action Recognition with Temporal Contrastive Learning","created":"2021-02-04","cited_by_count":50,"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":"2106.00168","title":"Rethinking Pseudo Labels for Semi-Supervised Object Detection","created":"2021-06-01","cited_by_count":45,"area":null},{"id":"2106.01432","title":"SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training","created":"2021-06-02","cited_by_count":45,"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":"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":"2311.16241","title":"SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance","created":"2023-11-27","cited_by_count":41,"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":"2112.05825","title":"Revisiting Consistency Regularization for Semi-Supervised Learning","created":"2021-12-10","cited_by_count":34,"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":"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":"2108.09412","title":"SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling","created":"2021-08-21","cited_by_count":29,"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":"2110.10232","title":"Test time Adaptation through Perturbation Robustness","created":"2021-10-19","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":"2302.02335","title":"Semi-Supervised Domain Adaptation with Source Label Adaptation","created":"2023-02-05","cited_by_count":26,"area":null},{"id":"2303.11066","title":"Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data","created":"2023-03-20","cited_by_count":26,"area":null},{"id":"2306.02061","title":"Balancing Logit Variation for Long-tailed Semantic Segmentation","created":"2023-06-03","cited_by_count":26,"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":"2111.11067","title":"Semi-Supervised Vision Transformers","created":"2021-11-22","cited_by_count":24,"area":null},{"id":"2110.11767","title":"Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning","created":"2021-10-22","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":"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":"2203.14316","title":"MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization","created":"2022-03-27","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":"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":"2410.02592","title":"IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers","created":"2024-10-03","cited_by_count":20,"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":"2204.05108","title":"Improved Training of Physics-Informed Neural Networks with Model Ensembles","created":"2022-04-11","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":"2504.03755","title":"ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery","created":"2025-04-02","cited_by_count":19,"area":null},{"id":"2112.12004","title":"Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images","created":"2021-12-22","cited_by_count":17,"area":null},{"id":"2308.05359","title":"Pseudo-label Alignment for Semi-supervised Instance Segmentation","created":"2023-08-10","cited_by_count":17,"area":null},{"id":"2403.11184","title":"DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentation","created":"2024-03-17","cited_by_count":17,"area":null},{"id":"2106.00209","title":"Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning","created":"2021-06-01","cited_by_count":16,"area":null},{"id":"2202.09059","title":"Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning","created":"2022-02-18","cited_by_count":16,"area":null},{"id":"2203.15765","title":"FisherMatch: Semi-Supervised Rotation Regression via Entropy-based Filtering","created":"2022-03-29","cited_by_count":16,"area":null},{"id":"2203.16038","title":"Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels","created":"2022-03-30","cited_by_count":16,"area":null},{"id":"2205.03873","title":"Multimodal Semi-Supervised Learning for Text Recognition","created":"2022-05-08","cited_by_count":16,"area":null},{"id":"2207.04998","title":"Consistency is the key to further mitigating catastrophic forgetting in continual learning","created":"2022-07-11","cited_by_count":16,"area":null},{"id":"2209.02178","title":"Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students","created":"2022-09-06","cited_by_count":16,"area":null},{"id":"2106.11853","title":"Credal Self-Supervised Learning","created":"2021-06-22","cited_by_count":15,"area":null},{"id":"2109.01087","title":"On-target Adaptation","created":"2021-09-02","cited_by_count":15,"area":null},{"id":"2210.09175","title":"Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization","created":"2022-10-17","cited_by_count":15,"area":null},{"id":"2108.09671","title":"Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift","created":"2021-08-22","cited_by_count":14,"area":null},{"id":"2101.06967","title":"On Data-Augmentation and Consistency-Based Semi-Supervised Learning","created":"2021-01-18","cited_by_count":13,"area":null},{"id":"2112.07194","title":"MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation","created":"2021-12-14","cited_by_count":13,"area":null},{"id":"2202.11782","title":"Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks","created":"2022-02-23","cited_by_count":13,"area":null},{"id":"2205.00504","title":"Domain Adaptation meets Individual Fairness. And they get along","created":"2022-05-01","cited_by_count":13,"area":null},{"id":"2303.09061","title":"MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection","created":"2023-03-16","cited_by_count":13,"area":null},{"id":"2303.16270","title":"Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples","created":"2023-03-28","cited_by_count":13,"area":null},{"id":"2103.10368","title":"MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels","created":"2021-03-18","cited_by_count":12,"area":null},{"id":"2403.01909","title":"Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey","created":"2024-03-04","cited_by_count":12,"area":null},{"id":"2407.04036","title":"Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier","created":"2024-07-04","cited_by_count":12,"area":null},{"id":"2111.11595","title":"Semi-Supervised Learning with Taxonomic Labels","created":"2021-11-23","cited_by_count":11,"area":null},{"id":"2204.00656","title":"Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes","created":"2022-04-01","cited_by_count":11,"area":null},{"id":"2302.06081","title":"Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval","created":"2023-02-13","cited_by_count":11,"area":null},{"id":"2308.07509","title":"Boosting Semi-Supervised Learning by bridging high and low-confidence predictions","created":"2023-08-15","cited_by_count":11,"area":null},{"id":"2309.13258","title":"Order-preserving Consistency Regularization for Domain Adaptation and Generalization","created":"2023-09-23","cited_by_count":11,"area":null},{"id":"2503.02231","title":"CGMatch: A Different Perspective of Semi-supervised Learning","created":"2025-03-04","cited_by_count":11,"area":null},{"id":"2112.04319","title":"SCR: Training Graph Neural Networks with Consistency Regularization","created":"2021-12-08","cited_by_count":10,"area":null},{"id":"2203.15353","title":"SIOD: Single Instance Annotated Per Category Per Image for Object Detection","created":"2022-03-29","cited_by_count":10,"area":null},{"id":"2209.05297","title":"DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification","created":"2022-09-12","cited_by_count":10,"area":null},{"id":"2305.13764","title":"Mitigating Label Noise through Data Ambiguation","created":"2023-05-23","cited_by_count":10,"area":null},{"id":"2308.09037","title":"MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins","created":"2023-08-17","cited_by_count":10,"area":null},{"id":"2309.10147","title":"Realistic Website Fingerprinting By Augmenting Network Trace","created":"2023-09-18","cited_by_count":10,"area":null},{"id":"2103.13696","title":"SSLayout360: Semi-Supervised Indoor Layout Estimation from 360-Degree Panorama","created":"2021-03-25","cited_by_count":9,"area":null},{"id":"2210.03594","title":"Label Propagation with Weak Supervision","created":"2022-10-07","cited_by_count":9,"area":null},{"id":"2407.09787","title":"Semi-supervised 3D Object Detection with PatchTeacher and PillarMix","created":"2024-07-13","cited_by_count":9,"area":null},{"id":"2110.11205","title":"Robustness through Data Augmentation Loss Consistency","created":"2021-10-21","cited_by_count":8,"area":null},{"id":"2203.00190","title":"Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey","created":"2022-03-01","cited_by_count":8,"area":null},{"id":"2205.13921","title":"Federated Semi-Supervised Learning with Prototypical Networks","created":"2022-05-27","cited_by_count":8,"area":null},{"id":"2205.15239","title":"Conformal Credal Self-Supervised Learning","created":"2022-05-30","cited_by_count":8,"area":null},{"id":"2206.02368","title":"Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation","created":"2022-06-06","cited_by_count":8,"area":null},{"id":"2303.00977","title":"Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning","created":"2023-03-02","cited_by_count":8,"area":null},{"id":"2306.02314","title":"Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation","created":"2023-06-04","cited_by_count":8,"area":null},{"id":"2310.15787","title":"SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning","created":"2023-10-24","cited_by_count":8,"area":null},{"id":"2312.01619","title":"How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking","created":"2023-12-04","cited_by_count":8,"area":null},{"id":"2403.11674","title":"Towards Generalizing to Unseen Domains with Few Labels","created":"2024-03-18","cited_by_count":8,"area":null}],"next_cursor":"100"}