Methods › Natural Language Processing › Transformers › GANformer

Generative Adversarial Transformer

GANformer

3 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

GANformer is a novel and efficient type of transformer which can be used for visual generative modeling. The network employs a bipartite structure that enables long-range interactions across an image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes.

Source: Generative Adversarial Transformers

Image source: Generative Adversarial Transformers

Source: Generative Adversarial Transformers

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Generation2
Disentanglement1
Image Manipulation1
Scene Generation1
Transfer Learning1

Usage over time archive 2025-07-28

Papers per year tagged with GANformer: 2021 to 2023, peak 2 2 0 2021: 1 paper 2021 2022: 0 papers 2022 2023: 2 papers 2023
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Transformers

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