# CollaGAN : Collaborative GAN for Missing Image Data Imputation

## TL;DR

This paper introduces CollaGAN, a novel multi-domain image-to-image translation framework using GANs, which effectively imputes missing image data with higher visual quality than existing methods.

## Contribution

The paper presents CollaGAN, a new collaborative GAN architecture that transforms missing image data imputation into a multi-domain translation problem, improving image quality.

## Key findings

- CollaGAN outperforms existing methods in visual quality of imputed images.
- The framework successfully handles various image imputation tasks.
- It converts the problem into a multi-domain translation, simplifying the process.

## Abstract

In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias. Although many techniques have been developed for imputing missing data, the image imputation is still difficult due to complicated nature of natural images. To address this problem, here we proposed a novel framework for missing image data imputation, called Collaborative Generative Adversarial Network (CollaGAN). CollaGAN converts an image imputation problem to a multi-domain images-to-image translation task so that a single generator and discriminator network can successfully estimate the missing data using the remaining clean data set. We demonstrate that CollaGAN produces the images with a higher visual quality compared to the existing competing approaches in various image imputation tasks.