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Handling Concept Drift in Time-Series Data: Meta-cognitive Recurrent Recursive-Kernel OS-ELM Read chapter Read first chapter

2018 | OriginalPaper | Chapter

TiedGAN: Multi-domain Image Transformation Networks

Authors : Mohammad Ahangar Kiasari, Dennis Singh Moirangthem, Jonghong Kim, Minho Lee

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Recently, domain transformation has become a popular challenge in deep generative networks. One of the recent well-known domain transformation model named CycleGAN, has shown good performance in transformation task from one domain to another domain. However, CycleGAN lacks the capability to address multi-domain transformation problems because of its high complexity. In this paper, we propose TiedGAN in order to achieve multi-domain image transformation with reduced complexity. The results of our experiment indicate that the proposed model has comparable performance to CycleGAN as well as successfully alleviates the complexity issue in the multi-domain transformation task.

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previous chapter Fast Image Recognition with Gabor Filter and Pseudoinverse Learning AutoEncoders
next chapter Graph Matching Based on Fast Normalized Cut
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Metadata
Title
TiedGAN: Multi-domain Image Transformation Networks
Authors
Mohammad Ahangar Kiasari
Dennis Singh Moirangthem
Jonghong Kim
Minho Lee
Copyright Year
2018
Book
Neural Information Processing

Print ISBN: 978-3-030-04223-3

Electronic ISBN: 978-3-030-04224-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-04224-0_44

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