{"url":"/method/anycost-gan","slug":"anycost-gan","name":"Anycost GAN","full_name":"Anycost GAN","full_name_withheld":false,"description_markdown":"**Anycost GAN** is a type of generative adversarial network for image synthesis and editing. Given an input image, we project it into the latent space with encoder $E$ and backward optimization. We can modify the latent code with user input to edit the image. During editing, a sub-generator of small cost is used for fast and interactive preview; during idle time, the full cost generator renders the final, high-quality output. The outputs from the full and sub-generators are visually consistent during projection and editing.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Anycost GANs for Interactive Image Synthesis and Editing","paper":"/paper/anycost-gans-for-interactive-image-synthesis","first_author":"Ji Lin","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/anycost-gans-for-interactive-image-synthesis"},"source":{"url":"https://arxiv.org/abs/2103.03243v1","title":"Anycost GANs for Interactive Image Synthesis and Editing","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/anycost-gans-for-interactive-image-synthesis","title":"Anycost GANs for Interactive Image Synthesis and Editing","date":"2021-03-04","arxiv_id":"2103.03243","n_code_links":1,"syntology":{"ran":0,"of":1,"unverified":1,"pointer_only":0}},{"paper":null,"title":"Interpretable Multiple Treatment Revenue Uplift Modeling","date":"2021-01-09","arxiv_id":"2101.03336","n_code_links":0,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/decision-making","name":"Decision Making","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/marketing","name":"Marketing","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2021","papers":2}],"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/anycost-gan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}