mlsanityData to 7 Oct 2026

Semi-Supervised and Unsupervised Deep Visual Learning: A Survey

arXiv 2208.11296 ↗First posted 24 Aug 2022cs.CV cs.AI cs.LGcited by 52

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Abstract (arXiv)

State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model's generalizability in the limited-label regime. Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data. Recent progress in these paradigms has indicated the strong benefits of leveraging unlabeled data to improve model generalization and provide better model initialization. In this survey, we review the recent advanced deep learning algorithms on semi-supervised learning (SSL) and unsupervised learning (UL) for visual recognition from a unified perspective. To offer a holistic understanding of the state-of-the-art in these areas, we propose a unified taxonomy. We categorize existing representative SSL and UL with comprehensive and insightful analysis to highlight their design rationales in different learning scenarios and applications in different computer vision tasks. Lastly, we discuss the emerging trends and open challenges in SSL and UL to shed light on future critical research directions.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). Builds on 220 of its 319 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

220 of 319 references.

References · 220

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50 most cited of 220 All 220 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 18; 2024: 13; 2025: 16; 2026: 3.

52 papers.

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  1. 2023Emergent Correspondence from Image Diffusioncited by 437
  2. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  3. 2023A comprehensive survey on deep active learning in medical image analysiscited by 32
  4. 2024CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignmentcited by 20
  5. 2023Uncertainty-inspired Open Set Learning for Retinal Anomaly Identificationcited by 18
  6. 2022A soft nearest-neighbor framework for continual semi-supervised learningcited by 17
  7. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  8. 2023Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Modelscited by 14
  9. 2023Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentationcited by 12
  10. 2022Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Datacited by 11
  11. 2025Parameter-Efficient Continual Fine-Tuning: A Surveycited by 11
  12. 2025Scene-Centric Unsupervised Panoptic Segmentationcited by 10
38 more
  1. 2023ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learningcited by 9
  2. 2023Systematic comparison of semi-supervised and self-supervised learning for medical image classificationcited by 8
  3. 2023Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learningcited by 7
  4. 2025A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learningcited by 7
  5. 2023Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenescited by 6
  6. 2024Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspectivecited by 6
  7. 2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Artcited by 6
  8. 2024Controllable Image Synthesis of Industrial Data Using Stable Diffusioncited by 5
  9. 2024Annotations on a Budget: Leveraging Geo-Data Similarity to Balance Model Performance and Annotation Costcited by 5
  10. 2025A survey of datasets for computer vision in agriculturecited by 5
  11. 2025Category Discovery: An Open-World Perspectivecited by 5
  12. 2024The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Datacited by 4
  13. 2024Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformercited by 4
  14. 2025SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognitioncited by 4
  15. 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3
  16. 2023SSASS: Semi-Supervised Approach for Stenosis Segmentationcited by 2
  17. 2023Physics-Informed Neural Networks for Discovering Localised Eigenstates in Disordered Mediacited by 1
  18. 2023Semi-Supervised Learning in the Few-Shot Zero-Shot Scenariocited by 1
  19. 2023Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentationcited by 1
  20. 2024Cross Domain Object Detection via Multi-Granularity Confidence Alignment based Mean Teachercited by 1
  21. 2024Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentationcited by 1
  22. 2025Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systemscited by 1
  23. 2026Practical Insights into Semi-Supervised Object Detection Approachescited by 1
  24. 2026FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentationcited by 1
  25. 2023Evaluating The Robustness of Self-Supervised Representations to Background/Foreground Removalcited by 0
  26. 2023Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimationcited by 0
  27. 2023Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognitioncited by 0
  28. 2024Machine Learning and Data Analysis Using Posets: A Surveycited by 0
  29. 2024SelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labelingcited by 0
  30. 2024GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distributioncited by 0
  31. 2024Theory-inspired Label Shift Adaptation via Aligned Distribution Mixturecited by 0
  32. 2025Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentationcited by 0
  33. 2025Semi-KAN: KAN Provides an Effective Representation for Semi-Supervised Learning in Medical Image Segmentationcited by 0
  34. 2025Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Surveycited by 0
  35. 2025Feature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiencycited by 0
  36. 2025nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentationcited by 0
  37. 2025Normality Calibration in Semi-supervised Graph Anomaly Detectioncited by 0
  38. 2025Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learningcited by 0

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