{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/renet-a-recurrent-neural-network-based","title":"ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks","arxiv_id":"1505.00393","date":"2015-05-03","proceeding":null,"authors":["Francesco Visin","Kyle Kastner","Kyunghyun Cho","Matteo Matteucci","Aaron Courville","Yoshua Bengio"],"abstract":"In this paper, we propose a deep neural network architecture for object\nrecognition based on recurrent neural networks. The proposed network, called\nReNet, replaces the ubiquitous convolution+pooling layer of the deep\nconvolutional neural network with four recurrent neural networks that sweep\nhorizontally and vertically in both directions across the image. We evaluate\nthe proposed ReNet on three widely-used benchmark datasets; MNIST, CIFAR-10 and\nSVHN. The result suggests that ReNet is a viable alternative to the deep\nconvolutional neural network, and that further investigation is needed.","url_abs":"http://arxiv.org/abs/1505.00393v3","url_pdf":"http://arxiv.org/pdf/1505.00393v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"renet-a-recurrent-neural-network-based","repo_url":"https://github.com/NisTa24/ReNet-Implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"renet-a-recurrent-neural-network-based","repo_url":"https://github.com/SConsul/ReSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"renet-a-recurrent-neural-network-based","repo_url":"https://github.com/brian-moser/dartsrenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"renet-a-recurrent-neural-network-based","repo_url":"https://github.com/fvisin/reseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-cifar-10","task":"Image Classification","dataset":"CIFAR-10","model":"ReNet","rank_in_archive_order":218,"of":265,"metrics":{"Percentage correct":"87.7"},"uses_additional_data":true},{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"ReNet","rank_in_archive_order":35,"of":81,"metrics":{"Percentage error":"0.5"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-svhn","task":"Image Classification","dataset":"SVHN","model":"ReNet","rank_in_archive_order":35,"of":62,"metrics":{"Percentage error":"2.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1505.00393","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}