{"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/multi-column-deep-neural-networks-for-image","title":"Multi-column Deep Neural Networks for Image Classification","arxiv_id":"1202.2745","date":"2012-02-13","proceeding":null,"authors":["Dan Cireşan","Ueli Meier","Juergen Schmidhuber"],"abstract":"Traditional methods of computer vision and machine learning cannot match\nhuman performance on tasks such as the recognition of handwritten digits or\ntraffic signs. Our biologically plausible deep artificial neural network\narchitectures can. Small (often minimal) receptive fields of convolutional\nwinner-take-all neurons yield large network depth, resulting in roughly as many\nsparsely connected neural layers as found in mammals between retina and visual\ncortex. Only winner neurons are trained. Several deep neural columns become\nexperts on inputs preprocessed in different ways; their predictions are\naveraged. Graphics cards allow for fast training. On the very competitive MNIST\nhandwriting benchmark, our method is the first to achieve near-human\nperformance. On a traffic sign recognition benchmark it outperforms humans by a\nfactor of two. We also improve the state-of-the-art on a plethora of common\nimage classification benchmarks.","url_abs":"http://arxiv.org/abs/1202.2745v1","url_pdf":"http://arxiv.org/pdf/1202.2745v1.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":"multi-column-deep-neural-networks-for-image","repo_url":"https://github.com/hughperkins/DeepCL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MPL-2.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"traffic-sign-recognition","task_name":"Traffic Sign Recognition"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-cifar-10","task":"Image Classification","dataset":"CIFAR-10","model":"MCDNN","rank_in_archive_order":213,"of":265,"metrics":{"Percentage correct":"88.8"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"MCDNN","rank_in_archive_order":8,"of":81,"metrics":{"Percentage error":"0.23"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1202.2745","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}