Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://arxiv.org/abs/1511.06434

University of Michigan - Ann Arbor

DCGANs are an improvement of GANs. They are more stable and generate higher quality images. In DCGAN, batch normalization is done in both networks, i.e the generator network and the discriminator network. They can be used for style transfer. For example, you can use a dataset of handbags to generate shoes in the same style as the handbags.

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