mlsanityData to 14 Oct 2026

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing

arXiv 2110.01442 ↗First posted 1 Oct 2021cs.LG cs.AI cs.CVcited by 13

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Abstract (arXiv)

We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different fields.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 102 of its 199 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

102 of 199 references.

References · 102

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
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  9. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  10. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
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  12. 2016Improved Techniques for Training GANscited by 2,970
38 more
  1. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  2. 2017Improved Training of Wasserstein GANscited by 2,235
  3. 2014Conditional Generative Adversarial Netscited by 2,162
  4. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  5. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
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  7. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
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  9. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
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  11. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,179
  12. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  13. 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,039
  14. 2016Least Squares Generative Adversarial Networkscited by 1,001
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  16. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
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  19. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
  20. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
  21. 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 524
  22. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 464
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  26. 2015Deep multi-scale video prediction beyond mean square errorcited by 300
  27. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  28. 2017Pose Guided Person Image Generationcited by 251
  29. 2017AttGAN: Facial Attribute Editing by Only Changing What You Wantcited by 242
  30. 2018Talking Face Generation by Adversarially Disentangled Audio-Visual Representationcited by 212
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  34. 2017Image De-raining Using a Conditional Generative Adversarial Networkcited by 197
  35. 2017Decomposing Motion and Content for Natural Video Sequence Predictioncited by 169
  36. 2017Deformable GANs for Pose-based Human Image Generationcited by 166
  37. 2016Semantic Image Inpainting with Deep Generative Modelscited by 157
  38. 2017Video Generation From Textcited by 154

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Citing papers per year: 2022: 1; 2023: 2; 2024: 5; 2025: 2; 2026: 3.

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  2. 2025FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mappingcited by 6
  3. 2024A Systematic Review of Federated Generative Modelscited by 5
  4. 2024An information theoretic limit to data amplificationcited by 2
  5. 2023Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Imagescited by 1
  6. 2024Early Stopping Criteria for Training Generative Adversarial Networks in Biomedical Imagingcited by 1
  7. 2024Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defectscited by 1
  8. 2026Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Modelscited by 1
  9. 2022Cyclegan Network for Sheet Metal Welding Drawing Translationcited by 0
  10. 2024DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networkscited by 0
  11. 2025Eye-See-You: Reverse Pass-Through VR and Head Avatarscited by 0
  12. 2026Synthetic data in cryptocurrencies using generative modelscited by 0
1 more
  1. 2026GenONet: A Generative operator Network for High-Resolution Precipitation Nowcastingcited by 0