Fundus to Angiography Generation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Image-to-Image Translation with Conditional Adversarial Networks
  21 Nov 2016 192 repositories listed Syntology ran 14 of 122 samples · 108 unverified · 1 pointer-only (licence)
  We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems.
• U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
  25 Jul 2019 28 repositories listed Syntology ran 5 of 36 samples · 31 unverified
  We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
  30 Nov 2017 21 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
• StarGAN v2: Diverse Image Synthesis for Multiple Domains
  4 Dec 2019 14 repositories listed Syntology ran 0 of 5 samples · 5 unverified
  A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains.
• VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers
  14 Apr 2021 2 repositories listed
  The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA).
• Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks
  17 Jul 2020 2 repositories listed
  Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.