mlsanityData to 7 Oct 2026

CAFLOW: Conditional Autoregressive Flows

arXiv 2106.02531 ↗First posted 4 Jun 2021cs.CV cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 41 of its 46 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

41 of 46 references.

References · 41

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29 more
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.

4 papers.

  1. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  2. 2023Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imagingcited by 4
  3. 2025An invertible generative model for forward and inverse problemscited by 1
  4. 2025MAGIC-Flow: Multiscale Adaptive Conditional Flows for Generation and Interpretable Classificationcited by 0