arXiv (Cornell University)· 2014· Preprint
Conditional Generative Adversarial Nets
- 8,916citations
- 2014year
Short summary
Conditional Generative Adversarial Nets (CGANs) can generate MNIST digits conditioned on class labels by feeding the conditioning data (y) to both the generator and discriminator.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced Conditional Generative Adversarial Nets (CGANs).
- CGANs condition generation by feeding data 'y' to both generator and discriminator.
- Demonstrated generation of MNIST digits conditioned on class labels.
- Showcased potential for multi-modal modeling and image tagging.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. arXiv (Cornell University), 2014 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science