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Learning Representations for Automatic Colorization
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“The auxiliary pretext is primarily used to fill in missing parts of an image [6], convert it to gray scale [7], predict hidden parts, and many other tasks.”
From Self-supervised learning: A succinct review (2023) · cites this paper as [7]
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Metadata
- Authors
- Larsson, G., Maire, M., & Shakhnarovich, G.
- Affiliations
- University of Chicago (US)
- Toyota Technological Institute at Chicago (US)
- Corresponding author
- Larsson, Gustav
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 577-593
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2308529009
- Cited by
- 946
- References
- 51
- Citations by year
- 2026: 25 · 2025: 45 · 2024: 75 · 2023: 109 · 2022: 179 · 2021: 176 · 2020: 133 · 2019: 93 · 2018: 63 · 2017: 43 · 2016: 5
- FWCI
- 23.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image colorization, automatic colorization, self-supervised visual representation learning, semantic representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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