mlsanityData to 14 Oct 2026

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

arXiv 1810.10863 ↗First posted 25 Oct 2018cs.CVcited by 107

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Abstract (arXiv)

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets. The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers. The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting. So far, much effort has been directed at extracting as much information as possible from what data is available. Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images. This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC) of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available.

Key facts. Cited by 107 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Oct 2018.

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Citing papers per year: 2021: 29; 2022: 21; 2023: 23; 2024: 15; 2025: 14; 2026: 5.

107 papers.

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  1. 2024UniScene: Unified Occupancy-centric Driving Scene Generationcited by 106
  2. 2022EnvEdit: Environment Editing for Vision-and-Language Navigationcited by 83
  3. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  4. 2022Synthesizing Informative Training Samples with GANcited by 77
  5. 2021Vision-Language Navigation with Random Environmental Mixupcited by 70
  6. 2022C-Mixup: Improving Generalization in Regressioncited by 57
  7. 2023GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generationcited by 56
  8. 2021Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentationcited by 52
  9. 2021Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentationcited by 43
  10. 2024A Comprehensive Survey on Data Augmentationcited by 40
  11. 2021Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classificationcited by 35
  12. 2024A survey of synthetic data augmentation methods in computer visioncited by 34
38 more
  1. 2024DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perceptioncited by 32
  2. 2021Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Imagescited by 31
  3. 2021OCT-GAN: Neural ODE-based Conditional Tabular GANscited by 27
  4. 2021Improving Fractal Pre-trainingcited by 26
  5. 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
  6. 2021Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classificationcited by 24
  7. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  8. 2022You Only Cut Once: Boosting Data Augmentation with a Single Cutcited by 22
  9. 2021Incentivizing Collaboration in Machine Learning via Synthetic Data Rewardscited by 20
  10. 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Modelcited by 19
  11. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19
  12. 2022DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detectioncited by 17
  13. 2023Fine-grained Recognition with Learnable Semantic Data Augmentationcited by 17
  14. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  15. 2021The Devil is in the GAN: Backdoor Attacks and Defenses in Deep Generative Modelscited by 12
  16. 2022Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learningcited by 12
  17. 2023Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritagecited by 12
  18. 2021Adversarial Imitation Learning with Trajectorial Augmentation and Correctioncited by 11
  19. 2023Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalancecited by 11
  20. 2023Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentationcited by 10
  21. 2024OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generationcited by 10
  22. 2025Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Methodcited by 8
  23. 2021Robust pedestrian detection in thermal imagery using synthesized imagescited by 7
  24. 2021Sill-Net: Feature Augmentation with Separated Illumination Representationcited by 7
  25. 2023Instance-Conditioned GAN Data Augmentation for Representation Learningcited by 7
  26. 2023Generative Adversarial Networks for Data Augmentationcited by 7
  27. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
  28. 2021Ensembles of GANs for synthetic training data generationcited by 6
  29. 2021Explainable Machine Learning using Real, Synthetic and Augmented Fire Tests to Predict Fire Resistance and Spalling of RC Columnscited by 6
  30. 2022Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classificationcited by 6
  31. 2022Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learningcited by 6
  32. 2021Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentationcited by 5
  33. 2021Generating Symbolic Reasoning Problems with Transformer GANscited by 5
  34. 2021Repaint: Improving the Generalization of Down-Stream Visual Tasks by Generating Multiple Instances of Training Examplescited by 5
  35. 2023Generating Synthetic Datasets by Interpolating along Generalized Geodesicscited by 5
  36. 2023ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentationcited by 5
  37. 2025FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesiscited by 5
  38. 2022Exploring the Intersection between Neural Architecture Search and Continual Learningcited by 4

50 most cited of 107 All 107 (JSON)