Continuous Conditional Generative Adversarial Networks (cGAN) with Generator Regularization

Abstract

Conditional Generative Adversarial Networks are known to be difficult to train, especially when the conditions are continuous and high-dimensional. To partially alleviate this difficulty, we propose a simple generator regularization term on the GAN generator loss in the form of Lipschitz penalty. Thus, when the generator is fed with neighboring conditions in the continuous space, the regularization term will leverage the neighbor information and push the generator to generate samples that have similar conditional distributions for each neighboring condition. We analyze the effect of the proposed regularization term and demonstrate its robust performance on a range of synthetic and real-world tasks.

References in corpus (8)

• Conditional Generative Adversarial Nets
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Improved Training of Wasserstein GANs
• Improved Techniques for Training GANs
• cGANs with Projection Discriminator
• Multimodal Unsupervised Image-to-Image Translation
• MirrorGAN: Learning Text-to-image Generation by Redescription
• Metrics for Deep Generative Models

Cited by in corpus (1)

• t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning