{"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/data-augmentation-via-levy-processes","title":"Data Augmentation via Levy Processes","arxiv_id":"1603.06340","date":"2016-03-21","proceeding":null,"authors":["Stefan Wager","William Fithian","Percy Liang"],"abstract":"If a document is about travel, we may expect that short snippets of the\ndocument should also be about travel. We introduce a general framework for\nincorporating these types of invariances into a discriminative classifier. The\nframework imagines data as being drawn from a slice of a Levy process. If we\nslice the Levy process at an earlier point in time, we obtain additional\npseudo-examples, which can be used to train the classifier. We show that this\nscheme has two desirable properties: it preserves the Bayes decision boundary,\nand it is equivalent to fitting a generative model in the limit where we rewind\ntime back to 0. Our construction captures popular schemes such as Gaussian\nfeature noising and dropout training, as well as admitting new generalizations.","url_abs":"http://arxiv.org/abs/1603.06340v1","url_pdf":"http://arxiv.org/pdf/1603.06340v1.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":"data-augmentation-via-levy-processes","repo_url":"https://github.com/swager/levythin","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}