{"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/feta-a-dca-pruning-algorithm-with","title":"FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees","arxiv_id":"1803.04239","date":"2018-03-12","proceeding":null,"authors":["Konstantinos Pitas","Mike Davies","Pierre Vandergheynst"],"abstract":"Recent DNN pruning algorithms have succeeded in reducing the number of\nparameters in fully connected layers, often with little or no drop in\nclassification accuracy. However, most of the existing pruning schemes either\nhave to be applied during training or require a costly retraining procedure\nafter pruning to regain classification accuracy. We start by proposing a cheap\npruning algorithm for fully connected DNN layers based on difference of convex\nfunctions (DC) optimisation, that requires little or no retraining. We then\nprovide a theoretical analysis for the growth in the Generalization Error (GE)\nof a DNN for the case of bounded perturbations to the hidden layers, of which\nweight pruning is a special case. Our pruning method is orders of magnitude\nfaster than competing approaches, while our theoretical analysis sheds light to\npreviously observed problems in DNN pruning. Experiments on commnon feedforward\nneural networks validate our results.","url_abs":"http://arxiv.org/abs/1803.04239v1","url_pdf":"http://arxiv.org/pdf/1803.04239v1.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":"feta-a-dca-pruning-algorithm-with","repo_url":"https://github.com/konstantinos-p/FeTa_Fully_Connected","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}