{"url":"/method/tsruc","slug":"tsruc","name":"TSRUc","full_name":"TSRUc","full_name_withheld":false,"description_markdown":"**TSRUc**, or **Transformation-based Spatial Recurrent Unit c**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.\r\n\r\nInstead of computing the reset gate $r$ and resetting $h\\_{t−1}$, the TSRUc computes the parameters of a transformation $\\theta$, which we use to warp $h\\_{t−1}$. The rest of our model is unchanged (with $\\hat{h}\\_{t-1}$ playing the role of $h'\\_{t}$ in $c$’s update equation from ConvGRU. The TSRUc module is described by the following equations:\r\n\r\n$$ \\theta\\_{h,x} = f\\left(h\\_{t−1}, x\\_{t}\\right) $$\r\n\r\n$$ \\hat{h}\\_{t-1} = w\\left(h\\_{t-1}; \\theta\\_{h, x}\\right) $$\r\n\r\n$$ c = \\rho\\left(W\\_{c} \\star\\_{n}\\left[\\hat{h}\\_{t-1};x\\_{t}\\right] + b\\_{c} \\right) $$\r\n\r\n$$ u = \\sigma\\left(W\\_{u} \\star\\_{n}\\left[h\\_{t-1};x\\_{t}\\right] + b\\_{u} \\right) $$\r\n\r\n$$ h\\_{t} = u \\odot h\\_{t-1} + \\left(1-u\\right) \\odot c $$\r\n\r\nIn these equations $\\sigma$ and $\\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\\star\\_{n}$ represents a [convolution](https://paperswithcode.com/method/convolution) with a kernel of size $n \\times n$. Brackets are used to represent a feature concatenation.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","paper":"/paper/transformation-based-adversarial-video","first_author":"Pauline Luc","n_authors":7,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/transformation-based-adversarial-video"},"source":{"url":"https://arxiv.org/abs/2003.04035v3","title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Sequential","area_id":"sequential","collection":"Recurrent Neural Networks","url":"/methods/category/recurrent-neural-networks","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/transformation-based-adversarial-video","title":"Transformation-based Adversarial Video Prediction on Large-Scale Data","date":"2020-03-09","arxiv_id":"2003.04035","n_code_links":0,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/video-generation","name":"Video Generation","papers":1},{"task":"/task/video-prediction","name":"Video Prediction","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","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/tsruc"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}