Updating the generator in PPGN-h with gradients flowing through the encoder

Updating the generator in PPGN-h with gradients flowing through the encoder

The Generative Adversarial Network framework has shown success in implicitly modeling data distributions and is able to generate realistic samples. Its architecture is comprised of a generator, which produces fake data that superficially seem to belong to the real data distribution, and a discrimina...

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Bibliographic Details
Main Author: Pakdaman, Hesam
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2018
Subjects:
Computer Science
Computer Vision
Deep Learning
Machine Learning
Generative Adversarial Networks
GAN
Neural Networks
Generative models
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-224867

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