{"url":"/method/u-net-gan","slug":"u-net-gan","name":"U-Net GAN","full_name":"U-Net Generative Adversarial Network","full_name_withheld":false,"description_markdown":"In contrast to typical GANs, a U-Net GAN uses a segmentation network as the discriminator. This segmentation network predicts two classes: real and fake. In doing so, the discriminator gives the generator region-specific feedback. This discriminator design also enables a  [CutMix](https://paperswithcode.com/method/cutmix)-based consistency regularization on the two-dimensional output of the U-Net GAN discriminator, which further improves image synthesis quality.","description_state":"present","introduced_year":null,"introduced_by":{"title":"A U-Net Based Discriminator for Generative Adversarial Networks","paper":"/paper/a-u-net-based-discriminator-for-generative-1","first_author":"Edgar Schonfeld","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/a-u-net-based-discriminator-for-generative-1"},"source":{"url":"http://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html","title":"A U-Net Based Discriminator for Generative Adversarial Networks","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial Networks","url":"/methods/category/generative-adversarial-networks","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/octave-2d-en-face-optical-coherence","title":"OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation","date":"2022-07-25","arxiv_id":"2207.12238","n_code_links":1,"syntology":null},{"paper":"/paper/a-u-net-based-discriminator-for-generative-1","title":"A U-Net Based Discriminator for Generative Adversarial Networks","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":1},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":1},{"task":"/task/retinal-vessel-segmentation","name":"Retinal Vessel Segmentation","papers":1},{"task":"/task/weakly-supervised-segmentation","name":"Weakly supervised segmentation","papers":1},{"task":"/task/weakly-supervised-learning","name":"Weakly-supervised Learning","papers":1}],"tasks_shown":6,"n_tasks":6,"usage_by_year":[{"year":"2020","papers":1},{"year":"2022","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/u-net-gan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}