{"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/deep-neural-nets-with-interpolating-function","title":"Deep Neural Nets with Interpolating Function as Output Activation","arxiv_id":"1802.00168","date":"2018-02-01","proceeding":"NeurIPS 2018 12","authors":["Bao Wang","Xiyang Luo","Zhen Li","Wei Zhu","Zuoqiang Shi","Stanley J. Osher"],"abstract":"We replace the output layer of deep neural nets, typically the softmax\nfunction, by a novel interpolating function. And we propose end-to-end training\nand testing algorithms for this new architecture. Compared to classical neural\nnets with softmax function as output activation, the surrogate with\ninterpolating function as output activation combines advantages of both deep\nand manifold learning. The new framework demonstrates the following major\nadvantages: First, it is better applicable to the case with insufficient\ntraining data. Second, it significantly improves the generalization accuracy on\na wide variety of networks. The algorithm is implemented in PyTorch, and code\nwill be made publicly available.","url_abs":"http://arxiv.org/abs/1802.00168v3","url_pdf":"http://arxiv.org/pdf/1802.00168v3.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":"deep-neural-nets-with-interpolating-function","repo_url":"https://github.com/BaoWangMath/DNN-DataDependentActivation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}