mlsanityData to 7 Oct 2026

Colorful Image Colorization

arXiv 1603.08511 ↗First posted 28 Mar 2016cs.CVcited by 1,113

Not on the map. Why?

Cited by 36 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 1,113 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Mar 2016.

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Citing papers per year: 2021: 359; 2022: 312; 2023: 228; 2024: 114; 2025: 64; 2026: 36.

1,113 papers.

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38 more
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