{"total":226,"rows":[{"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":"2208.07204","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","created":"2022-08-12","cited_by_count":95,"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":"2202.12040","title":"Self-Training: A Survey","created":"2022-02-24","cited_by_count":86,"area":null},{"id":"2306.05980","title":"Federated Learning for Medical Image Analysis: A Survey","created":"2023-06-09","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":"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":"2202.07141","title":"Machine Learning in Aerodynamic Shape Optimization","created":"2022-02-15","cited_by_count":39,"area":null},{"id":"2408.14371","title":"SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery","created":"2024-08-26","cited_by_count":34,"area":null},{"id":"2503.04207","title":"Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior","created":"2025-03-06","cited_by_count":29,"area":null},{"id":"2206.02353","title":"Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data","created":"2022-06-06","cited_by_count":27,"area":null},{"id":"2301.05500","title":"RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation","created":"2023-01-13","cited_by_count":27,"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":"2311.17286","title":"LEOD: Label-Efficient Object Detection for Event Cameras","created":"2023-11-29","cited_by_count":24,"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":"2211.09233","title":"Prompt Tuning for Parameter-efficient Medical Image Segmentation","created":"2022-11-16","cited_by_count":22,"area":null},{"id":"2305.08824","title":"Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement","created":"2023-05-15","cited_by_count":22,"area":null},{"id":"2406.13378","title":"PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation","created":"2024-06-19","cited_by_count":22,"area":null},{"id":"2305.19812","title":"A Survey of Label-Efficient Deep Learning for 3D Point Clouds","created":"2023-05-31","cited_by_count":20,"area":null},{"id":"2110.00726","title":"Domain-Specific Bias Filtering for Single Labeled Domain Generalization","created":"2021-10-02","cited_by_count":18,"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":"2202.12123","title":"An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift","created":"2022-02-24","cited_by_count":18,"area":null},{"id":"2309.16772","title":"XVO: Generalized Visual Odometry via Cross-Modal Self-Training","created":"2023-09-28","cited_by_count":18,"area":null},{"id":"2203.04251","title":"End-to-End Semi-Supervised Learning for Video Action Detection","created":"2022-03-08","cited_by_count":17,"area":null},{"id":"2302.09899","title":"A Survey on Semi-Supervised Semantic Segmentation","created":"2023-02-20","cited_by_count":17,"area":null},{"id":"2308.01587","title":"Consistency Regularization for Generalizable Source-free Domain Adaptation","created":"2023-08-03","cited_by_count":17,"area":null},{"id":"2308.13862","title":"Late Stopping: Avoiding Confidently Learning from Mislabeled Examples","created":"2023-08-26","cited_by_count":17,"area":null},{"id":"2206.00309","title":"Label-Efficient Online Continual Object Detection in Streaming Video","created":"2022-06-01","cited_by_count":16,"area":null},{"id":"2303.12484","title":"Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions","created":"2023-03-22","cited_by_count":16,"area":null},{"id":"2408.07221","title":"A Review of Pseudo-Labeling for Computer Vision","created":"2024-08-13","cited_by_count":16,"area":null},{"id":"2409.16434","title":"Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition","created":"2024-09-24","cited_by_count":16,"area":null},{"id":"2203.14272","title":"Discovering Human-Object Interaction Concepts via Self-Compositional Learning","created":"2022-03-27","cited_by_count":15,"area":null},{"id":"2304.05729","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","created":"2023-04-12","cited_by_count":15,"area":null},{"id":"2301.03726","title":"Neighborhood-Regularized Self-Training for Learning with Few Labels","created":"2023-01-10","cited_by_count":14,"area":null},{"id":"2308.00994","title":"SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems","created":"2023-08-02","cited_by_count":13,"area":null},{"id":"2412.11174","title":"Semi-Supervised Risk Control via Prediction-Powered Inference","created":"2024-12-15","cited_by_count":13,"area":null},{"id":"2302.11962","title":"Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods","created":"2023-02-23","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":"2206.14923","title":"On Non-Random Missing Labels in Semi-Supervised Learning","created":"2022-06-29","cited_by_count":11,"area":null},{"id":"2406.09896","title":"Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation","created":"2024-06-14","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":"2212.08380","title":"Instance-dependent Label Distribution Estimation for Learning with Label Noise","created":"2022-12-16","cited_by_count":10,"area":null},{"id":"2312.07169","title":"Semi-supervised Active Learning for Video Action Detection","created":"2023-12-12","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":"2403.03145","title":"Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization","created":"2024-03-05","cited_by_count":10,"area":null},{"id":"2311.16420","title":"Model-free Test Time Adaptation for Out-Of-Distribution Detection","created":"2023-11-28","cited_by_count":9,"area":null},{"id":"2312.05286","title":"Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors","created":"2023-12-08","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":"2203.12707","title":"Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation","created":"2022-03-23","cited_by_count":8,"area":null},{"id":"2401.12790","title":"MORPH: Towards Automated Concept Drift Adaptation for Malware Detection","created":"2024-01-23","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},{"id":"2407.19332","title":"A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention","created":"2024-07-27","cited_by_count":8,"area":null},{"id":"2111.06021","title":"Probabilistic Contrastive Learning for Domain Adaptation","created":"2021-11-11","cited_by_count":7,"area":null},{"id":"2201.01230","title":"Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones","created":"2022-01-03","cited_by_count":7,"area":null},{"id":"2405.05241","title":"BenthicNet: A global compilation of seafloor images for deep learning applications","created":"2024-05-08","cited_by_count":7,"area":null},{"id":"2301.05158","title":"SemPPL: Predicting pseudo-labels for better contrastive representations","created":"2023-01-12","cited_by_count":6,"area":null},{"id":"2310.06557","title":"Data efficient deep learning for medical image analysis: A survey","created":"2023-10-10","cited_by_count":6,"area":null},{"id":"2310.10352","title":"Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2412.00613","title":"A Unified Data Representation Learning for Non-parametric Two-sample Testing","created":"2024-11-30","cited_by_count":6,"area":null},{"id":"2503.09707","title":"Revisiting semi-supervised learning in the era of foundation models","created":"2025-03-12","cited_by_count":6,"area":null},{"id":"2203.01687","title":"Structure-Guided Self-Supervised Matching for One-Shot Medical Landmark Detection","created":"2022-03-03","cited_by_count":5,"area":null},{"id":"2204.09186","title":"Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion","created":"2022-04-20","cited_by_count":5,"area":null},{"id":"2211.11753","title":"SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels","created":"2022-11-20","cited_by_count":5,"area":null},{"id":"2302.00722","title":"A Survey of Deep Learning: From Activations to Transformers","created":"2023-02-01","cited_by_count":5,"area":null},{"id":"2303.05775","title":"Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields","created":"2023-03-10","cited_by_count":5,"area":null},{"id":"2305.10018","title":"Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers","created":"2023-05-17","cited_by_count":5,"area":null},{"id":"2306.06010","title":"A Large-Scale Analysis on Self-Supervised Video Representation Learning","created":"2023-06-09","cited_by_count":5,"area":null},{"id":"2306.06982","title":"Weakly Supervised Lesion Detection and Diagnosis for Breast Cancers with Partially Annotated Ultrasound Images","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2306.14106","title":"Semi-supervised Object Detection: A Survey on Recent Research and Progress","created":"2023-06-25","cited_by_count":5,"area":null},{"id":"2306.16036","title":"A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans","created":"2023-06-28","cited_by_count":5,"area":null},{"id":"2312.03318","title":"Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift","created":"2023-12-06","cited_by_count":5,"area":null},{"id":"2106.11892","title":"Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation","created":"2021-06-22","cited_by_count":4,"area":null},{"id":"2111.06181","title":"Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training","created":"2021-11-11","cited_by_count":4,"area":null},{"id":"2204.12833","title":"Transfer Learning with Pre-trained Conditional Generative Models","created":"2022-04-27","cited_by_count":4,"area":null},{"id":"2207.06817","title":"Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning","created":"2022-07-14","cited_by_count":4,"area":null},{"id":"2208.03142","title":"BoxShrink: From Bounding Boxes to Segmentation Masks","created":"2022-08-05","cited_by_count":4,"area":null},{"id":"2210.09919","title":"Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks","created":"2022-10-18","cited_by_count":4,"area":null},{"id":"2308.11757","title":"Weakly Supervised Face and Whole Body Recognition in Turbulent Environments","created":"2023-08-22","cited_by_count":4,"area":null},{"id":"2401.02429","title":"Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities","created":"2023-11-13","cited_by_count":4,"area":null},{"id":"2406.02774","title":"Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following","created":"2024-06-04","cited_by_count":4,"area":null},{"id":"2407.05088","title":"Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation","created":"2024-07-06","cited_by_count":4,"area":null},{"id":"2407.08460","title":"Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer","created":"2024-07-11","cited_by_count":4,"area":null},{"id":"2502.17673","title":"Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments","created":"2025-02-24","cited_by_count":4,"area":null},{"id":"2503.20697","title":"Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization","created":"2025-03-26","cited_by_count":4,"area":null},{"id":"2504.13186","title":"Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection","created":"2025-03-30","cited_by_count":4,"area":null},{"id":"2506.08005","title":"ZeroVO: Visual Odometry with Minimal Assumptions","created":"2025-06-09","cited_by_count":4,"area":null},{"id":"2110.13388","title":"Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design","created":"2021-10-26","cited_by_count":3,"area":null},{"id":"2202.05975","title":"Robust Deep Semi-Supervised Learning: A Brief Introduction","created":"2022-02-12","cited_by_count":3,"area":null},{"id":"2212.00024","title":"Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation","created":"2022-11-30","cited_by_count":3,"area":null},{"id":"2302.08659","title":"Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling","created":"2023-02-17","cited_by_count":3,"area":null},{"id":"2306.07621","title":"Rank-Aware Negative Training for Semi-Supervised Text Classification","created":"2023-06-13","cited_by_count":3,"area":null},{"id":"2312.06343","title":"RankMatch: A Novel Approach to Semi-Supervised Label Distribution Learning Leveraging Inter-label Correlations","created":"2023-12-11","cited_by_count":3,"area":null},{"id":"2401.07463","title":"Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian","created":"2024-01-15","cited_by_count":3,"area":null},{"id":"2403.11234","title":"Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias","created":"2024-03-17","cited_by_count":3,"area":null},{"id":"2404.17489","title":"Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based Augmentation","created":"2024-04-26","cited_by_count":3,"area":null},{"id":"2407.06698","title":"PSPU: Enhanced Positive and Unlabeled Learning by Leveraging Pseudo Supervision","created":"2024-07-09","cited_by_count":3,"area":null},{"id":"2408.00386","title":"What comes after transformers? -- A selective survey connecting ideas in deep learning","created":"2024-08-01","cited_by_count":3,"area":null},{"id":"2503.00884","title":"Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model","created":"2025-03-02","cited_by_count":3,"area":null},{"id":"2504.06153","title":"A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning","created":"2025-04-08","cited_by_count":3,"area":null},{"id":"2506.05917","title":"Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness","created":"2025-06-06","cited_by_count":3,"area":null}],"next_cursor":"100"}