We introduce CAFLOW, a new diverse image-to-image translation model that simultaneously leverages the power of auto-regressive modeling and the modeling efficiency of conditional normalizing flows. We transform the conditioning image into a sequence of latent encodings using a multi-scale normalizing flow and repeat the process for the conditioned image. We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale. Our proposed framework performs well on a range of image-to-image translation tasks. It outperforms former designs of conditional flows because of its expressive auto-regressive structure.
@article{arxiv.2106.02531,
title = {CAFLOW: Conditional Autoregressive Flows},
author = {Georgios Batzolis and Marcello Carioni and Christian Etmann and Soroosh Afyouni and Zoe Kourtzi and Carola Bibiane Schönlieb},
journal= {arXiv preprint arXiv:2106.02531},
year = {2021}
}