{"total":247,"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":"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":"2201.02609","title":"Generalized Category Discovery","created":"2022-01-07","cited_by_count":211,"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":"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":"2110.14051","title":"A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges","created":"2021-10-26","cited_by_count":154,"area":null},{"id":"2202.03423","title":"Backdoor Defense via Decoupling the Training Process","created":"2022-02-05","cited_by_count":153,"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":"2101.08629","title":"Image-to-Image Translation: Methods and Applications","created":"2021-01-21","cited_by_count":116,"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":"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":"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":"2208.07204","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","created":"2022-08-12","cited_by_count":95,"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":"2210.03310","title":"Scaling Forward Gradient With Local Losses","created":"2022-10-07","cited_by_count":65,"area":null},{"id":"2209.14733","title":"Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights","created":"2022-09-29","cited_by_count":64,"area":"distill"},{"id":"2208.11296","title":"Semi-Supervised and Unsupervised Deep Visual Learning: A Survey","created":"2022-08-24","cited_by_count":52,"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":"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":"2105.00957","title":"Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning","created":"2021-05-03","cited_by_count":43,"area":null},{"id":"2111.08446","title":"Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions","created":"2021-11-03","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":"2305.18287","title":"LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections","created":"2023-05-29","cited_by_count":43,"area":null},{"id":"2403.06726","title":"Probabilistic Contrastive Learning for Long-Tailed Visual Recognition","created":"2024-03-11","cited_by_count":42,"area":null},{"id":"2109.04145","title":"PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition","created":"2021-09-09","cited_by_count":41,"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":"2302.10586","title":"Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels","created":"2023-02-21","cited_by_count":37,"area":"diffusion"},{"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":"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":"2308.12595","title":"Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation","created":"2023-08-24","cited_by_count":29,"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":"2309.11433","title":"A Systematic Review of Few-Shot Learning in Medical Imaging","created":"2023-09-20","cited_by_count":28,"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":"2303.11066","title":"Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data","created":"2023-03-20","cited_by_count":26,"area":null},{"id":"2308.09281","title":"Diverse Cotraining Makes Strong Semi-Supervised Segmentor","created":"2023-08-18","cited_by_count":26,"area":null},{"id":"2111.11067","title":"Semi-Supervised Vision Transformers","created":"2021-11-22","cited_by_count":24,"area":null},{"id":"2202.05613","title":"CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning","created":"2022-02-11","cited_by_count":24,"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":"2110.07197","title":"Semi-supervised Multi-task Learning for Semantics and Depth","created":"2021-10-14","cited_by_count":22,"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":"2103.09002","title":"Hebbian Semi-Supervised Learning in a Sample Efficiency Setting","created":"2021-03-16","cited_by_count":19,"area":null},{"id":"2304.05395","title":"SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence","created":"2023-04-10","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":"2301.13865","title":"From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds","created":"2023-01-31","cited_by_count":18,"area":null},{"id":"2303.05079","title":"DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection","created":"2023-03-09","cited_by_count":18,"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":"2203.06127","title":"Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations","created":"2022-03-11","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":"2108.08467","title":"Medical Image Segmentation with 3D Convolutional Neural Networks: A Survey","created":"2021-08-19","cited_by_count":16,"area":null},{"id":"2205.10095","title":"How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing","created":"2022-05-20","cited_by_count":16,"area":null},{"id":"2206.00845","title":"Hyperspherical Consistency Regularization","created":"2022-06-02","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":"2111.15210","title":"Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining","created":"2021-11-30","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":"2108.06649","title":"Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling","created":"2021-08-15","cited_by_count":14,"area":null},{"id":"2310.03013","title":"SemiReward: A General Reward Model for Semi-supervised Learning","created":"2023-10-04","cited_by_count":14,"area":"alignment"},{"id":"2104.00543","title":"RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning","created":"2021-03-31","cited_by_count":13,"area":null},{"id":"2206.04846","title":"Masked Autoencoders are Robust Data Augmentors","created":"2022-06-10","cited_by_count":13,"area":null},{"id":"2303.15023","title":"ScarceNet: Animal Pose Estimation with Scarce Annotations","created":"2023-03-27","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":"2107.10064","title":"Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition","created":"2021-07-21","cited_by_count":12,"area":null},{"id":"2110.11998","title":"Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations","created":"2021-10-22","cited_by_count":12,"area":null},{"id":"2205.14361","title":"Boosting Facial Expression Recognition by A Semi-Supervised Progressive Teacher","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2207.10951","title":"Hyper-Representations for Pre-Training and Transfer Learning","created":"2022-07-22","cited_by_count":12,"area":"distill"},{"id":"2211.03850","title":"Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding","created":"2022-11-07","cited_by_count":12,"area":null},{"id":"2308.08872","title":"Towards Semi-supervised Learning with Non-random Missing Labels","created":"2023-08-17","cited_by_count":12,"area":null},{"id":"2503.06277","title":"STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification","created":"2025-03-08","cited_by_count":12,"area":null},{"id":"2109.04533","title":"FedCon: A Contrastive Framework for Federated Semi-Supervised Learning","created":"2021-09-09","cited_by_count":11,"area":null},{"id":"2111.11595","title":"Semi-Supervised Learning with Taxonomic Labels","created":"2021-11-23","cited_by_count":11,"area":null},{"id":"2305.14258","title":"Weakly Supervised AUC Optimization: A Unified Partial AUC Approach","created":"2023-05-23","cited_by_count":11,"area":null},{"id":"2111.00201","title":"A Comparative Review of Recent Few-Shot Object Detection Algorithms","created":"2021-10-30","cited_by_count":10,"area":null},{"id":"2401.16424","title":"Computer Vision for Primate Behavior Analysis in the Wild","created":"2024-01-29","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":"2105.04637","title":"Local Frequency Domain Transformer Networks for Video Prediction","created":"2021-05-10","cited_by_count":9,"area":null},{"id":"2109.00778","title":"Better Self-training for Image Classification through Self-supervision","created":"2021-09-02","cited_by_count":9,"area":null},{"id":"2110.11236","title":"Variational Predictive Routing with Nested Subjective Timescales","created":"2021-10-21","cited_by_count":9,"area":null},{"id":"2209.12611","title":"MaxMatch: Semi-Supervised Learning with Worst-Case Consistency","created":"2022-09-26","cited_by_count":9,"area":null},{"id":"2401.07551","title":"Robust Semi-Supervised Learning for Self-learning Open-World Classes","created":"2024-01-15","cited_by_count":9,"area":null},{"id":"2006.09107","title":"Learning from Demonstration with Weakly Supervised Disentanglement","created":"2020-06-16","cited_by_count":8,"area":"offlinerl"},{"id":"2102.07358","title":"Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator","created":"2021-02-15","cited_by_count":8,"area":null},{"id":"2205.05575","title":"DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision","created":"2022-05-11","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":"2303.00977","title":"Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning","created":"2023-03-02","cited_by_count":8,"area":null},{"id":"2303.07048","title":"Hybrid Variational Autoencoder for Time Series Forecasting","created":"2023-03-13","cited_by_count":8,"area":null},{"id":"2305.10397","title":"RelationMatch: Matching In-batch Relationships for Semi-supervised Learning","created":"2023-05-17","cited_by_count":8,"area":null},{"id":"2308.13900","title":"Semi-Supervised Semantic Segmentation via Marginal Contextual Information","created":"2023-08-26","cited_by_count":8,"area":null},{"id":"2310.09620","title":"Machine Learning for Urban Air Quality Analytics: A Survey","created":"2023-10-14","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":"2105.11293","title":"WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection","created":"2021-05-21","cited_by_count":7,"area":null},{"id":"2109.05612","title":"FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning","created":"2021-09-12","cited_by_count":7,"area":null},{"id":"2109.07049","title":"Self-Training with Differentiable Teacher","created":"2021-09-15","cited_by_count":7,"area":"distill"},{"id":"2111.10932","title":"Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation","created":"2021-11-22","cited_by_count":7,"area":null},{"id":"2305.00771","title":"Towards Unbiased Training in Federated Open-world Semi-supervised Learning","created":"2023-05-01","cited_by_count":7,"area":null}],"next_cursor":"100"}