# Learning Diverse Image Colorization

Source: [Learning Diverse Image Colorization](<https://www.sota2.com/research/paper/68fa634b1e209131ce9fdc7f>)

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

Data updated: 2025-10-25T20:37:15.413Z

Authors: Aditya Deshpande, Jiajun Lu, Mao-Chuang Yeh, Min Jin Chong, David Forsyth

Published: 2016-12-06T19:28:22.000Z

## Abstract

Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However, previous methods only produce the single most probable colorization. Our goal is to model the diversity intrinsic to the problem of colorization and produce multiple colorizations that display long-scale spatial co-ordination. We learn a low dimensional embedding of color fields using a variational autoencoder (VAE). We construct loss terms for the VAE decoder that avoid blurry outputs and take into account the uneven distribution of pixel colors. Finally, we build a conditional model for the multi-modal distribution between grey-level image and the color field embeddings. Samples from this conditional model result in diverse colorization. We demonstrate that our method obtains better diverse colorizations than a standard conditional variational autoencoder (CVAE) model, as well as a recently proposed conditional generative adversarial network (cGAN).

## 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: 25.98.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1612.01958>)

[PDF](<https://arxiv.org/pdf/1612.01958.pdf>)