# ComboGAN: Unrestrained Scalability for Image Domain Translation Source: [ComboGAN: Unrestrained Scalability for Image Domain Translation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:53:10.657Z Authors: Asha Anoosheh, Eirikur Agustsson, Radu Timofte, Luc Van Gool Published: 2017-12-19T13:18:42.000Z ## Abstract This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator/discriminator pairs; whereas our model requires only 14 generator/discriminator pairs. ## 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. - [Cross-contrast MR image translation on BraTS 2019 (test)](): Cross-contrast MR image translation; BraTS 2019 (test); MAE: 0.0081. - [Cross-contrast MR image translation on IXI (test)](): Cross-contrast MR image translation; IXI (test); MAE: 0.0163. ## Original sources and implementation [Original paper]() [PDF]()