Authors: Jeongik Cho, Kyoungro Yoon
Conditional Generative Adversarial Network (GAN) is a GAN that generates data with the desired condition from the latent vector. The auxiliary classifier GAN is the most used among the variations of conditional GANs. In this study, we explain the problem of auxiliary classifier GAN and propose conditional activation GAN that can replace auxiliary classifier GAN to reduce the number of hyperparameters and improve training speed. The loss function of conditional activation GAN is defined as the sum of the loss of each GAN created for each condition. Since each GAN shares all hidden layers, the GANs can be considered as a single GAN and it does not increase the amount of computation much. Also, in order to prevent ignorance of conditions in the discriminator of conditional GANs with batch normalization, we propose a mixed batch training, in which each batch for discriminator is always configured to have the same ratio of real data and generated data so that each batch always has the similar condition distribution
Comments: 9 Pages.
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[v1] 2019-12-11 02:54:35
[v2] 2019-12-21 01:03:19
[v3] 2020-01-22 22:25:37
[v4] 2020-02-23 07:38:20
[v5] 2020-06-28 21:47:44
[v6] 2020-07-05 00:18:26
[v7] 2020-10-10 08:53:27
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