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Results tagged with gan
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user 32882
GAN refers to Generative Adversarial Networks. Such networks is made of two networks that compete against each other. The first one generates new samples and the second one discriminates between generated samples and true samples.
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Training the Discriminative Model in Generative Adversarial Neural Network
What I know so far in DCGAN is that a discriminator is trained using the labeled data (so maybe that occurs before training the generative model). Also, I know that there is race between the generator …
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Loss function in GAN
Since the aim of a Discriminator is to output 1 for real data and 0 for fake data, hence, the aim is to increase the likelihood of true data vs. fake one. In addition, since maximizing the likelihood …
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What is the purpose of the discriminator in an adversarial autoencoder?
This is specific to the generative adversarial network (GAN) proposed in A. Makhzani et al. "Adversarial Autoencoders". … In a traditional GAN, the discriminator is trained to distinguish real samples in $p(x)$ from fake generated samples output by the generator. …
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