{"url":"/method/ganformer","slug":"ganformer","name":"GANformer","full_name":"Generative Adversarial Transformer","full_name_withheld":false,"description_markdown":"GANformer is a novel and efficient type of [transformer](https://paperswithcode.com/method/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.\r\n\r\nSource: [Generative Adversarial Transformers](https://arxiv.org/pdf/2103.01209v2.pdf)\r\n\r\nImage source: [Generative Adversarial Transformers](https://arxiv.org/pdf/2103.01209v2.pdf)","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2103.01209v4","title":"Generative Adversarial Transformers","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Transformers","url":"/methods/category/transformers","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model","date":"2023-03-15","arxiv_id":"2303.08577","n_code_links":0,"syntology":null},{"paper":null,"title":"MorphGANFormer: Transformer-based Face Morphing and De-Morphing","date":"2023-02-18","arxiv_id":"2302.09404","n_code_links":0,"syntology":null},{"paper":"/paper/generative-adversarial-transformers","title":"Generative Adversarial Transformers","date":"2021-03-01","arxiv_id":"2103.01209","n_code_links":2,"syntology":{"ran":5,"of":6,"unverified":1,"pointer_only":0}}],"papers_shown":3,"tasks":[{"task":"/task/image-generation","name":"Image Generation","papers":2},{"task":"/task/disentanglement","name":"Disentanglement","papers":1},{"task":"/task/image-manipulation","name":"Image Manipulation","papers":1},{"task":"/task/scene-generation","name":"Scene Generation","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1}],"tasks_shown":5,"n_tasks":5,"usage_by_year":[{"year":"2021","papers":1},{"year":"2023","papers":2}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/ganformer"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}