O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
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@article{su2019gan,
title={O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks},
author={Su, Jianlin},
journal={arXiv preprint arXiv:1903.01931},
year={2019}
}
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Hi, im confused about the t2_loss. What does it do here?
x_fake_ng = Lambda(K.stop_gradient)(x_fake)
z_fake = e_model(x_fake)
z_fake_ng = e_model(x_fake_ng)
z_fake_mean = K.mean(z_fake, 1, keepdims=True)
z_fake_ng_mean = K.mean(z_fake_ng, 1, keepdims=True)
t2_loss = z_fake_mean - z_fake_ng_mean
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