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Found 3 papers, 1 papers with code
Effect of The Latent Structure on Clustering with GANs
We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and (iii) imposition of the desired cluster priors on the latent space.
MaskAAE: Latent space optimization for Adversarial Auto-Encoders
The field of neural generative models is dominated by the highly successful Generative Adversarial Networks (GANs) despite their challenges, such as training instability and mode collapse.
Mode matching in GANs through latent space learning and inversion
Generative adversarial networks (GANs) have shown remarkable success in generation of unstructured data, such as, natural images.
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