{"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/regularization-of-neural-networks-using","title":"Regularization of Neural Networks using DropConnect","arxiv_id":null,"date":"2013-06-13","proceeding":"ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28 2013 6","authors":["Li Wan","Matthew Zeiler","Sixin Zhang","Yann Lecun","Rob Fergus"],"abstract":"We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.","url_abs":"http://cds.nyu.edu/projects/regularization-neural-networks-using-dropconnect/","url_pdf":"http://yann.lecun.com/exdb/publis/pdf/wan-icml-13.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":"regularization-of-neural-networks-using","repo_url":"https://github.com/mlpack/mlpack/blob/master/src/mlpack/methods/ann/layer/dropconnect.hpp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"}],"methods":[{"method_slug":"dropconnect","method_name":"DropConnect"}],"datasets_introduced":[],"methods_introduced":[{"slug":"dropconnect","name":"DropConnect","full_name":"DropConnect"}],"results":[{"leaderboard":"/sota/image-classification-on-mnist","task":"Image Classification","dataset":"MNIST","model":"DropConnect","rank_in_archive_order":7,"of":81,"metrics":{"Accuracy":"99.77","Percentage error":"0.21"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}