Conditional Generative Adversarial Nets
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Cited by 229 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.
Key facts. Cited by 2,162 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2014.
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Citing papers per year: 2019: 2; 2020: 3; 2021: 535; 2022: 449; 2023: 455; 2024: 339; 2025: 257; 2026: 122.
2,162 papers.
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