Adversarial Latent Autoencoders

Adversarial Latent Autoencoders

podgorskiy/ALAE

[CVPR2020] Adversarial Latent Autoencoders . Contribute to podgorskiy/ALAE development by creating an account on GitHub.

Abstract: Autoencoder networks are unsupervised approaches aiming at combining generative and representational properties by learning simultaneously an encoder-generator map. Although studied extensively, the issues of whether they have the same generative power of GANs, or learn disentangled representations, have not been fully addressed. We introduce an autoencoder that tackles these issues jointly, which we call Adversarial Latent Autoencoder (ALAE).

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