CAFLOW: Conditional Autoregressive Flows

• URL: http://arxiv.org/abs/2106.02531v1
• Date: Fri, 4 Jun 2021 14:57:41 GMT
• Title: CAFLOW: Conditional Autoregressive Flows
• Authors: Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola Bibiane Sch\"onlieb
• Abstract summary: We introduce CAFLOW, a new diverse image-to-image translation model. We transform the conditioning image into a sequence of latent encodings using a multi-scale normalizing flow. Our proposed framework performs well on a range of image-to-image translation tasks.
• Score: 1.2233362977312945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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