mlsanityData to 7 Oct 2026

A Comprehensive Survey of Image Augmentation Techniques for Deep Learning

arXiv 2205.01491 ↗First posted 3 May 2022cs.CVcited by 95

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Abstract (arXiv)

Deep learning has been achieving decent performance in computer vision requiring a large volume of images, however, collecting images is expensive and difficult in many scenarios. To alleviate this issue, many image augmentation algorithms have been proposed as effective and efficient strategies. Understanding current algorithms is essential to find suitable methods or develop novel techniques for given tasks. In this paper, we perform a comprehensive survey on image augmentation for deep learning with a novel informative taxonomy. To get the basic idea why we need image augmentation, we introduce the challenges in computer vision tasks and vicinity distribution. Then, the algorithms are split into three categories; model-free, model-based, and optimizing policy-based. The model-free category employs image processing methods while the model-based method leverages trainable image generation models. In contrast, the optimizing policy-based approach aims to find the optimal operations or their combinations. Furthermore, we discuss the current trend of common applications with two more active topics, leveraging different ways to understand image augmentation, such as group and kernel theory, and deploying image augmentation for unsupervised learning. Based on the analysis, we believe that our survey gives a better understanding helpful to choose suitable methods or design novel algorithms for practical applications.

Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). Builds on 80 of its 116 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

80 of 116 references.

References · 80

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38 more
  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  2. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
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  14. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  15. 2017Random Erasing Data Augmentationcited by 1,235
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  18. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,062
  19. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  20. 2020Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentationcited by 595
  21. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
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  23. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 16; 2024: 34; 2025: 25; 2026: 14.

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  5. 2023A Holistic Assessment of the Reliability of Machine Learning Systemscited by 9
  6. 2024A Survey of the Self Supervised Learning Mechanisms for Vision Transformerscited by 8
  7. 2024FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learningcited by 8
  8. 2023DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentationcited by 7
  9. 2024AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentationcited by 7
  10. 2022Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approachcited by 6
  11. 2025From Easy to Hard: Building a Shortcut for Differentially Private Image Synthesiscited by 6
  12. 2025When Dynamic Data Selection Meets Data Augmentationcited by 6
  13. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  14. 2024Synthetic Image Learning: Preserving Performance and Preventing Membership Inference Attackscited by 5
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  18. 2022Effective Vision Transformer Training: A Data-Centric Perspectivecited by 3
  19. 2023TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Miningcited by 3
  20. 2024Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systemscited by 3
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  22. 2023STEM Rebalance: A Novel Approach for Tackling Imbalanced Datasets using SMOTE, Edited Nearest Neighbour, and Mixupcited by 2
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  24. 2024Adaptive Label Smoothing for Out-of-Distribution Detectioncited by 2
  25. 2024Cityscape-Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion-Based Image Editingcited by 2
  26. 2024Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetrycited by 2
  27. 2025ForAug: Mitigating Biases in Image Classification via Controlled Image Compositionscited by 2
  28. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
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  30. 2023AMDNet23: A combined deep Contour-based Convolutional Neural Network and Long Short Term Memory system to diagnose Age-related Macular Degenerationcited by 1
  31. 2023Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detectioncited by 1
  32. 2024Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillancecited by 1
  33. 2024Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methodscited by 1
  34. 2024Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagerycited by 1
  35. 2024ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generationcited by 1
  36. 2024Dynamic Feature Learning and Matching for Class-Incremental Learningcited by 1
  37. 2024Label Augmentation for Neural Networks Robustnesscited by 1
  38. 2024Deep-Wide Learning Assistance for Insect Pest Classificationcited by 1

50 most cited of 95 All 95 (JSON)