# Unsupervised Diverse Colorization via Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1702.06674 · HTML: https://mlsanity.com/paper/1702.06674/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 30 of 30)

- [Colorization Transformer](https://mlsanity.com/paper/2102.04432/) (2021)
- [Towards Vivid and Diverse Image Colorization with Generative Color Prior](https://mlsanity.com/paper/2108.08826/) (2021)
- [DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders](https://mlsanity.com/paper/2212.11613/) (2022)
- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization](https://mlsanity.com/paper/2112.01335/) (2021)
- [MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2105.14110/) (2021)
- [Wavelet Transform-assisted Adaptive Generative Modeling for Colorization](https://mlsanity.com/paper/2107.04261/) (2021)
- [SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution](https://mlsanity.com/paper/2201.04364/) (2022)
- [UniColor: A Unified Framework for Multi-Modal Colorization with Transformer](https://mlsanity.com/paper/2209.11223/) (2022)
- [Colorizing Monochromatic Radiance Fields](https://mlsanity.com/paper/2402.12184/) (2024)
- [Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies](https://mlsanity.com/paper/2108.06515/) (2021)
- [Learning Task-Independent Game State Representations from Unlabeled Images](https://mlsanity.com/paper/2206.06490/) (2022)
- [L-CAD: Language-based Colorization with Any-level Descriptions using Diffusion Priors](https://mlsanity.com/paper/2305.15217/) (2023)
- [Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual Approximators](https://mlsanity.com/paper/2512.23463/) (2025)
- [CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis](https://mlsanity.com/paper/2211.14286/) (2022)
- [StyleFlow For Content-Fixed Image to Image Translation](https://mlsanity.com/paper/2207.01909/) (2022)
- [Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization](https://mlsanity.com/paper/2210.14270/) (2022)
- [Hiding Behind Backdoors: Self-Obfuscation Against Generative Models](https://mlsanity.com/paper/2201.09774/) (2022)
- [Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data](https://mlsanity.com/paper/2504.09899/) (2025)
- [Disentangled Unsupervised Image Translation via Restricted Information Flow](https://mlsanity.com/paper/2111.13279/) (2021)
- [Analysis of Different Losses for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02980/) (2022)
- [Cross-Camera Deep Colorization](https://mlsanity.com/paper/2209.01211/) (2022)
- [Deep auxiliary learning for visual localization using colorization task](https://mlsanity.com/paper/2107.00222/) (2021)
- [Influence of Color Spaces for Deep Learning Image Colorization](https://mlsanity.com/paper/2204.02850/) (2022)
- [Human vs Objective Evaluation of Colourisation Performance](https://mlsanity.com/paper/2204.05200/) (2022)
- [See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images](https://mlsanity.com/paper/2210.07594/) (2022)
- [DualVAE: Controlling Colours of Generated and Real Images](https://mlsanity.com/paper/2305.18769/) (2023)
- [Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain](https://mlsanity.com/paper/2401.11425/) (2024)
- [LatentColorization: Latent Diffusion-Based Speaker Video Colorization](https://mlsanity.com/paper/2405.05707/) (2024)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)

## Abstract (arXiv)

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.