The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Colorful Image Colorization
Open accessAbstract
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32 % of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.
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Cites
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Self-citation
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Cited by
Unpaired image-to-image translation using cycle-consistent adversarial networks
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Self-citationImage-to-Image Translation with Conditional Adversarial Networks
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Self-citationThe Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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Self-citationMomentum Contrast for Unsupervised Visual Representation Learning
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Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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Metadata
- Authors
- Zhang, R., Isola, P., & Efros, A. A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 649-666
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2326925005
- Cited by
- 3,178
- References
- 15
- FWCI
- 70.48
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
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