Image-to-image translation is a task that has boomed under the more recent deep neural network architectures such as GANs. While many of these tasks require difficult to create paired datasets, the CycleGAN architecture allows the generation of such models using unpaired data. In this case, I chose to create a system that could translate between faces and digitally created avatars.
Results are still being improved, more to come.
