{"url":"/method/tgan","slug":"tgan","name":"TGAN","full_name":"TGAN","full_name_withheld":false,"description_markdown":"**TGAN** is a type of generative adversarial network that is capable of learning representation from an unlabeled video dataset and producing a new video. The generator consists of two sub networks\r\ncalled a temporal generator and an image generator. Specifically, the temporal generator first yields a set of latent variables, each of which corresponds to a latent variable for the image generator. Then, the image generator transforms these latent variables into a video which has the same number of frames as the variables. The model comprised of the temporal and image generators can not only enable to efficiently capture the time series, but also be easily extended to frame interpolation. The authors opt for a [WGAN](https://paperswithcode.com/method/wgan) as the basic [GAN](https://paperswithcode.com/method/gan) structure and objective, but use [singular value clipping](https://paperswithcode.com/method/singular-value-clipping) to enforce the Lipschitz constraint.","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":"http://arxiv.org/abs/1611.06624v3","title":"Temporal Generative Adversarial Nets with Singular Value Clipping","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/pfnet-research/tgan","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Models","url":"/methods/category/generative-models","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"DTGAN: Differential Private Training for Tabular GANs","date":"2021-07-06","arxiv_id":"2107.02521","n_code_links":0,"syntology":null},{"paper":"/paper/tabular-gans-for-uneven-distribution","title":"Tabular GANs for uneven distribution","date":"2020-10-01","arxiv_id":"2010.00638","n_code_links":1,"syntology":null},{"paper":"/paper/temporal-generative-adversarial-nets-with","title":"Temporal Generative Adversarial Nets with Singular Value Clipping","date":"2016-11-21","arxiv_id":"1611.06624","n_code_links":4,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}}],"papers_shown":3,"tasks":[{"task":"/task/attribute","name":"Attribute","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/tabular-data-generation","name":"Tabular Data Generation","papers":1},{"task":"/task/video-generation","name":"Video Generation","papers":1}],"tasks_shown":4,"n_tasks":4,"usage_by_year":[{"year":"2016","papers":1},{"year":"2020","papers":1},{"year":"2021","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/tgan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}