{"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/one-class-convolutional-neural-network","title":"One-Class Convolutional Neural Network","arxiv_id":"1901.08688","date":"2019-01-24","proceeding":null,"authors":["Poojan Oza","Vishal M. Patel"],"abstract":"We present a novel Convolutional Neural Network (CNN) based approach for one\nclass classification. The idea is to use a zero centered Gaussian noise in the\nlatent space as the pseudo-negative class and train the network using the\ncross-entropy loss to learn a good representation as well as the decision\nboundary for the given class. A key feature of the proposed approach is that\nany pre-trained CNN can be used as the base network for one class\nclassification. The proposed One Class CNN (OC-CNN) is evaluated on the\nUMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are\nrelated to a variety of one class application problems such as user\nauthentication, abnormality detection and novelty detection. Extensive\nexperiments demonstrate that the proposed method achieves significant\nimprovements over the recent state-of-the-art methods. The source code is\navailable at : github.com/otkupjnoz/oc-cnn.","url_abs":"http://arxiv.org/abs/1901.08688v1","url_pdf":"http://arxiv.org/pdf/1901.08688v1.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":"one-class-convolutional-neural-network","repo_url":"https://github.com/otkupjnoz/oc-cnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"one-class-convolutional-neural-network","repo_url":"https://github.com/1010code/OneClass_NeuralNetwork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"one-class-convolutional-neural-network","repo_url":"https://github.com/JuneKyu/CLAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"one-class-convolutional-neural-network","repo_url":"https://github.com/aotumanbiu/OC-NN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"novelty-detection","task_name":"Novelty Detection"},{"task_slug":"one-class-classification","task_name":"One-Class Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.08688","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}