NDLI: SCGN: novel generative model using the convergence of latent space by training

Content Provider	IET Digital Library
Author	Kim, H. H. Jung, S. H.
Abstract	Generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have been recently applied to various fields. However, the VAE and GAN models have blur and mode collapse problems, respectively. Here, the authors propose a novel generative model, self-converging generative network (SCGN), to address the issues. Self-converging means the convergence of latent vectors into themselves through being trained in pairs with training data, by which the SCGN can reconstruct all training data. In the authors’ model, the latent vectors and weights of the generator are alternately trained. Specifically, the latent vectors are trained to follow a normal distribution, using a loss function derived from the Kullback–Leibler divergence and a pixel-wise loss. The weights of the generator are adjusted for the generator to produce training data by means of a pixel-wise loss. As a result, their SCGN did not fall into the mode collapse, which occurs in GANs, and made clearer images than VAEs thanks to no use of sampling. Moreover, the SCGN successfully learned the manifold of the dataset in the extensive experiments with CelebA.
Starting Page	879
Ending Page	881
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 17, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/17
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1333
Journal	Electronics Letters
Publisher Date	2020-06-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Computer Vision And Image Processing Technique GAN Generative Adversarial Network Generative Model Image Resolution Knowledge Engineering Technique Kullback–Leibler Divergence Latent Space Latent Vector Learning in AI Mode Collapse Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Pixel-wise Loss SCGN Self-converging Generative Network Self-converging Means Statistics Training Data VAE
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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