Related Experiment Video
Updated: Oct 19, 2026

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
Published on: December 10, 2014
CAFLOW: Conditional Autoregressive Flows
Georgios Batzolis1, Marcello Carioni1, Christian Etmann1
1DAMTP, University of Cambridge, Cambridge CB3 0WA.
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.
Related Concept Videos
Rapidly Varying Flow
Introduction to Types of Flows
Two-dimensional flow involves changes in both length and height, as seen in air...
Gradually Varying Flow
Bernoulli's Equation for Flow Along a Streamline
Uniform Depth Channel Flow
Steady Flow of a Fluid Stream
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...