  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.
