Look Ma, No GANs! Image Transformation with ModifAE

Chad Atalla, Bartholomew Tam, Amanda Song, Gary Cottrell

Abstract: Existing methods of image to image translation require multiple steps in the training or modification process, and suffer from either an inability to generalize, or long training times. These methods also focus on binary trait modification, ignoring continuous traits. To address these problems, we propose ModifAE: a novel standalone neural network, trained exclusively on an autoencoding task, that implicitly learns to make continuous trait image modifications. As a standalone image modification network, ModifAE requires fewer parameters and less time to train than existing models. We empirically show that ModifAE produces significantly more convincing and more consistent continuous face trait modifications than the previous state-of-the-art model.

Keywords: Computer Vision, Deep Learning, Autoencoder, GAN, Image Modification, Social Traits, Social Psychology

TL;DR: ModifAE is a standalone neural network, trained exclusively on an autoencoding task, that implicitly learns to make image modifications (without GANs).

Data: [CelebA](https://paperswithcode.com/dataset/celeba)