Swapping Autoencoder for Deep Image Manipulation T aesung Park 12 Jun-Y an Zhu 2 Oliver Wang 2 Jingwan Lu2

We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling. The key idea is to encode an image into two independent components and enforce that any swapped combination maps to a realistic image.