Generative Adversarial Networks: An Overview
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Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image superresolution, and classification. The aim of this review article is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.
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GAN is a deep generative model for learning latent representation and producing artificial datasets of original datasets.
“The generative adversarial network (GAN) is another deep generative model for learning the latent representation and producing artificial datasets of the original datasets [58].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [58]
GANs mainly consist of two models: a generator G and a discriminator D.
“Generative Adversarial Networks (GANs) [45,71,79] mainly consist of two models: a generator G and a discriminator D.”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [45]
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Tang, Y., Zhang, N., Ciancia, M., & Wang, Z. (2024) · Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing · doi:10.1145/3678884.3681890 · Cited by 31 · see citing works
Effective near-duplicate image detection using perceptual hashing and deep learning
Jakhar, Y., & Borah, M. D. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104086 · Cited by 17 · see citing works
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
Si, S., Wang, J., Qu, X., Cheng, N., Wei, W., Zhu, X., & Xiao, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-1996 · Cited by 12 · see citing works
Neshaei, S. P., Rietsche, R., Su, X., & Wambsganss, T. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645169 · Cited by 9 · see citing works
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Sarridis, I., Spangenberg, J., Papadopoulou, O., & Papadopoulos, S. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2313890 · Cited by 8 · see citing works
Barahona, D., Breen, K. H., Kalesse-Los, H., & Röttenbacher, J. (2024) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-23-0025.1 · Cited by 4 · see citing works
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Chen, Q., & Heydari, B. (2025) · Journal of Mechanical Design · doi:10.1115/1.4068796 · Cited by 4 · see citing works
PVGAN: a generative adversarial network for object simplification in prosthetic vision
Elnabawy, R. H., Abdennadher, S., Hellwich, O., & Eldawlatly, S. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac8acf · Cited by 3 · see citing works
Harnessing GANs for Zero-Shot Learning of New Classes in Visual Speech Recognition
Kumar, Y., Sahrawat, D., Maheshwari, S., Mahata, D., Stent, A., Yin, Y., Ratn Shah, R., & Zimmermann, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5649 · Cited by 1 · see citing works
- Authors
- Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A.
- Affiliations
- Imperial College London (GB)
- School of Design, University of Wellington, Victoria, New Zealand
- Université de Montréal (CA)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2018-01
- Volume
- 35
- Issue
- 1
- Pages
- 53-65
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2765811365
- Cited by
- 4,540
- References
- 58
- Citations by year
- 2026: 383 · 2025: 859 · 2024: 1165 · 2023: 862 · 2022: 556 · 2021: 329 · 2020: 198 · 2019: 135 · 2018: 49 · 2017: 3
- FWCI
- 88.90
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Massachusetts Institute of Technology
- University of Georgia
- Imperial College London
- Victoria University of Wellington
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- generative adversarial networks, unsupervised learning, deep representations, image generation, semantic image editing, style transfer, image super-resolution, GAN architectures
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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