Ensembles of Generative Adversarial Networks for Disconnected Data

Lorenzo Luzi; Randall Balestriero; Richard G. Baraniuk

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arXiv · 2006.14600

Ensembles of Generative Adversarial Networks for Disconnected Data

Lorenzo Luzi·Randall Balestriero·Richard G. Baraniuk

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Abstract

Most current computer vision datasets are composed of disconnected sets, such as images from different classes. We prove that distributions of this type of data cannot be represented with a continuous generative network without error. They can be represented in two ways: With an ensemble of networks or with a single network with truncated latent space. We show that ensembles are more desirable than truncated distributions in practice. We construct a regularized optimization problem that establishes the relationship between a single continuous GAN, an ensemble of GANs, conditional GANs, and Gaussian Mixture GANs. This regularization can be computed efficiently, and we show empirically that our framework has a performance sweet spot which can be found with hyperparameter tuning. This ensemble framework allows better performance than a single continuous GAN or cGAN while maintaining fewer total parameters.

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Lorenzo Luzi, Randall Balestriero, Richard G. Baraniuk. 2020-06-25. Ensembles of Generative Adversarial Networks for Disconnected Data. https://arxiv.org/abs/2006.14600

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