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Title:Build and train advanced model for image generation
Author(s):Lin, Meng
Contributor(s):Lazebnik, Svetlana
Subject(s):advanced model of image generation
VAE-GAN model
Variational Autoencoder (VAN) Generative Adversarial Network model
Abstract:Recent developments on deep learning have enabled generative models to capture distribution of relatively complex datasets. In this research we aim to build a cutting-edge model that is able to learn the distribution of the CelebA face dataset. We surveyed several papers published in recent years and decided to construct and improve the VAE-GAN model, which combines the Variational Autoencoder (VAE) and Generative Adversarial Network (GAN). Chapter 1 introduces GAN, VAE, VAE-GAN model and addresses a disadvantage of GAN and proposes some methods to improve it. We also introduce Adversarial Autoencoder which we intend to accompany the model in the future. The other chapters address some details of implementation and show the results of the experiment.
Issue Date:2017-05
Genre:Other
Type:Text
Language:English
URI:http://hdl.handle.net/2142/97867
Date Available in IDEALS:2017-08-22


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