{"url":"/method/cgru","slug":"cgru","name":"CGRU","full_name":"Convolutional GRU","full_name_withheld":false,"description_markdown":"A **Convolutional Gated Recurrent Unit** is a type of [GRU](https://paperswithcode.com/method/gru) that combines GRUs with the [convolution](https://paperswithcode.com/method/convolution) operation. The update rule for input $x\\_{t}$ and the previous output $h\\_{t-1}$ is given by the following:\r\n\r\n$$ r = \\sigma\\left(W\\_{r} \\star\\_{n}\\left[h\\_{t-1};x\\_{t}\\right] + b\\_{r}\\right) $$\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$$ c = \\rho\\left(W\\_{c} \\star\\_{n}\\left[x\\_{t}; r \\odot h\\_{t-1}\\right] + b\\_{c} \\right) $$\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 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":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"http://arxiv.org/abs/1511.06432v4","title":"Delving Deeper into Convolutional Networks for Learning Video Representations","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":2,"archive_num_papers":null,"papers_newest_first":[{"paper":"/paper/efficient-video-generation-on-complex","title":"Adversarial Video Generation on Complex Datasets","date":"2019-07-15","arxiv_id":"1907.06571","n_code_links":1,"syntology":null},{"paper":"/paper/delving-deeper-into-convolutional-networks","title":"Delving Deeper into Convolutional Networks for Learning Video Representations","date":"2015-11-19","arxiv_id":"1511.06432","n_code_links":6,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/3d-character-animation-from-a-single-photo","name":"3D Character Animation From A Single Photo","papers":1},{"task":"/task/action-recognition-in-videos","name":"Action Recognition","papers":1},{"task":"/task/decoder","name":"Decoder","papers":1},{"task":"/task/action-recognition","name":"Temporal Action Localization","papers":1},{"task":"/task/video-captioning","name":"Video Captioning","papers":1},{"task":"/task/video-generation","name":"Video Generation","papers":1},{"task":"/task/video-prediction","name":"Video Prediction","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2015","papers":1},{"year":"2019","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/cgru"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}