Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks

We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space... (read more)

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Methods used in the Paper


METHOD TYPE
Memory Network
Working Memory Models
Convolution
Convolutions
GAN
Generative Models