Conditional Generative Adversarial Nets
TLDR
This paper introduces Conditional Generative Adversarial Nets (cGANs), which extend GANs by conditioning both generator and discriminator on auxiliary information, enabling controlled data generation.
Key contributions
- Proposes conditioning GANs on additional data (e.g., class labels) to guide the generation process.
- Demonstrates generation of MNIST digits conditioned on class labels, improving control over outputs.
- Explores applications such as multi-modal modeling and image tagging with generated descriptive tags beyond training labels.
Why it matters
By incorporating conditional information into GANs, this work enables more precise and controllable generative models, which is crucial for tasks requiring specific outputs like labeled image synthesis and multi-modal data generation. This advancement broadens the applicability of GANs in practical scenarios such as image annotation and conditional data generation.
Original 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.
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