# Unsupervised Diverse Colorization via Generative Adversarial Networks

Source: [Unsupervised Diverse Colorization via Generative Adversarial Networks](<https://www.sota2.com/research/paper/68fa636c1e209131ce9feab0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:41:16.530Z

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

Published: 2017-02-22T04:34:31.000Z

## Abstract

Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored 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 of 80 humans further indicates our generated color schemes are highly convincible.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Colorization on ImageNet (val)](<https://www.sota2.com/research/sota/image-colorization-on-imagenet-val.md>): Image Colorization; ImageNet (val); FID: 24.41.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1702.06674>)

[PDF](<https://arxiv.org/pdf/1702.06674.pdf>)