Tensorizing Generative Adversarial Nets

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Authors Xuyang Zhao, Xingwei Cao, Qibin Zhao
Journal/Conference Name 2018 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2018
Paper Category
Paper Abstract Generative Adversarial Network (GAN) and its variants exhibit state-of-the-art performance in the class of generative models. To capture higher-dimensional distributions, the common learning procedure requires high computational complexity and a large number of parameters. The problem of employing such massive framework arises when deploying it on a platform with limited computational power such as mobile phones. In this paper, we present a new generative adversarial framework by representing each layer as a tensor structure connected by multilinear operations, aiming to reduce the number of model parameters by a large factor while preserving the generative performance and sample quality. To learn the model, we employ an efficient algorithm which alternatively optimizes both discriminator and generator. Experimental outcomes demonstrate that our model can achieve high compression rate for model parameters up to $35$ times when compared to the original GAN for MNIST dataset.
Date of publication 2017
Code Programming Language Python
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