{"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/robust-classification-with-convolutional","title":"Robust Classification with Convolutional Prototype Learning","arxiv_id":"1805.03438","date":"2018-05-09","proceeding":"CVPR 2018 6","authors":["Hong-Ming Yang","Xu-Yao Zhang","Fei Yin","Cheng-Lin Liu"],"abstract":"Convolutional neural networks (CNNs) have been widely used for image\nclassification. Despite its high accuracies, CNN has been shown to be easily\nfooled by some adversarial examples, indicating that CNN is not robust enough\nfor pattern classification. In this paper, we argue that the lack of robustness\nfor CNN is caused by the softmax layer, which is a totally discriminative model\nand based on the assumption of closed world (i.e., with a fixed number of\ncategories). To improve the robustness, we propose a novel learning framework\ncalled convolutional prototype learning (CPL). The advantage of using\nprototypes is that it can well handle the open world recognition problem and\ntherefore improve the robustness. Under the framework of CPL, we design\nmultiple classification criteria to train the network. Moreover, a prototype\nloss (PL) is proposed as a regularization to improve the intra-class\ncompactness of the feature representation, which can be viewed as a generative\nmodel based on the Gaussian assumption of different classes. Experiments on\nseveral datasets demonstrate that CPL can achieve comparable or even better\nresults than traditional CNN, and from the robustness perspective, CPL shows\ngreat advantages for both the rejection and incremental category learning\ntasks.","url_abs":"http://arxiv.org/abs/1805.03438v1","url_pdf":"http://arxiv.org/pdf/1805.03438v1.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":"robust-classification-with-convolutional","repo_url":"https://github.com/YangHM/Convolutional-Prototype-Learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"robust-classification-with-convolutional","repo_url":"https://github.com/shivgahlout/Robust-Classification-with-Convolutional-Prototype-Learning-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"robust-classification-with-convolutional","repo_url":"https://github.com/zhen-cheng121/cpn_ova_unified","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"robust-classification","task_name":"Robust classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.03438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.03438"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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