NDLI: SOGN: novel generative model using SOM

Content Provider	IET Digital Library
Author	Kim, Ho Joong Jung, Sung Hoon
Abstract	Generative models such as variational autoencoder (VAE) and generative adversarial network (GAN) have been widely applied to many areas including image synthesis and voice generation. However, they have some problems that VAE makes blur images and GAN is difficult to learn due to mode collapsing. A novel generative model is proposed using a self-organising map (SOM) termed a self-organising generative network (SOGN). In the SOGN, training images are first mapped to SOM and then the output space of SOM is transformed into 2D vector spaces. These vector values are used as latent vectors to train the generative network such as artificial neural networks or convolutional neural networks. Experimental results with MNIST and CIFAR-10 datasets showed that their generative model was easy to train without mode collapsing and made more clean images than VAE. It was also confirmed that the manifold is well-observed without generating by the average effect of multiple images.
Starting Page	597
Ending Page	599
Page Count	3
ISSN	00135194
Volume Number	55
e-ISSN	1350911X
Issue Number	Issue 10, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/55/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.0202
Journal	Electronics Letters
Publisher Date	2019-04-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Areas Including Image Synthesis Blur Image Computer Vision And Image Processing Technique Feature Extraction GAN Generative Model Image Classification Image Representation Knowledge Engineering Technique Learning in AI Mode Collapsing Neural Computing Technique Neural Nets Object Detection Optical, Image And Video Signal Processing Self Organising Feature Maps Self-organising Generative Network SOGN SOM Statistics Training Image VAE Voice Generation
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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