{"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-over-sampling-framework-for-classifying","title":"Deep Over-sampling Framework for Classifying Imbalanced Data","arxiv_id":"1704.07515","date":"2017-04-25","proceeding":null,"authors":["Shin Ando","Chun-Yuan Huang"],"abstract":"Class imbalance is a challenging issue in practical classification problems\nfor deep learning models as well as traditional models. Traditionally\nsuccessful countermeasures such as synthetic over-sampling have had limited\nsuccess with complex, structured data handled by deep learning models. In this\npaper, we propose Deep Over-sampling (DOS), a framework for extending the\nsynthetic over-sampling method to exploit the deep feature space acquired by a\nconvolutional neural network (CNN). Its key feature is an explicit, supervised\nrepresentation learning, for which the training data presents each raw input\nsample with a synthetic embedding target in the deep feature space, which is\nsampled from the linear subspace of in-class neighbors. We implement an\niterative process of training the CNN and updating the targets, which induces\nsmaller in-class variance among the embeddings, to increase the discriminative\npower of the deep representation. We present an empirical study using public\nbenchmarks, which shows that the DOS framework not only counteracts class\nimbalance better than the existing method, but also improves the performance of\nthe CNN in the standard, balanced settings.","url_abs":"http://arxiv.org/abs/1704.07515v3","url_pdf":"http://arxiv.org/pdf/1704.07515v3.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-over-sampling-framework-for-classifying","repo_url":"https://github.com/m-zayan/DOS-Framework-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.07515","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}