{"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-transfer-learning-for-multiple-class","title":"Deep Transfer Learning for Multiple Class Novelty Detection","arxiv_id":"1903.02196","date":"2019-03-06","proceeding":"CVPR 2019 6","authors":["Pramuditha Perera","Vishal M. Patel"],"abstract":"We propose a transfer learning-based solution for the problem of multiple\nclass novelty detection. In particular, we propose an end-to-end deep-learning\nbased approach in which we investigate how the knowledge contained in an\nexternal, out-of-distributional dataset can be used to improve the performance\nof a deep network for visual novelty detection. Our solution differs from the\nstandard deep classification networks on two accounts. First, we use a novel\nloss function, membership loss, in addition to the classical cross-entropy loss\nfor training networks. Secondly, we use the knowledge from the external dataset\nmore effectively to learn globally negative filters, filters that respond to\ngeneric objects outside the known class set. We show that thresholding the\nmaximal activation of the proposed network can be used to identify novel\nobjects effectively. Extensive experiments on four publicly available novelty\ndetection datasets show that the proposed method achieves significant\nimprovements over the state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1903.02196v1","url_pdf":"http://arxiv.org/pdf/1903.02196v1.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-transfer-learning-for-multiple-class","repo_url":"https://github.com/PramuPerera/TransferLearningNovelty","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"caffe2","reach":{"status":"ok"}}],"tasks":[{"task_slug":"novelty-detection","task_name":"Novelty Detection"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.02196","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}