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Concentration Free Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Unsupervised Diverse Colorization via Generative Adversarial Networks

Authors : Yun Cao, Zhiming Zhou, Weinan Zhang, Yong Yu

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.

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previous chapter Scatteract: Automated Extraction of Data from Scatter Plots
next chapter Dynamic Ensemble Selection with Probabilistic Classifier Chains
Footnotes
1
Experiment code is available at https://​github.​com/​ccyyatnet/​COLORGAN.
 
2
LSUN dataset is available at http://​lsun.​cs.​princeton.​edu.
 
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Metadata
Title
Unsupervised Diverse Colorization via Generative Adversarial Networks
Authors
Yun Cao
Zhiming Zhou
Weinan Zhang
Yong Yu
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71248-2

Electronic ISBN: 978-3-319-71249-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71249-9_10

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