{"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/collaboratively-weighting-deep-and-classic","title":"Collaboratively Weighting Deep and Classic Representation via L2 Regularization for Image Classification","arxiv_id":"1802.07589","date":"2018-02-21","proceeding":null,"authors":["Shaoning Zeng","Bob Zhang","Yanghao Zhang","Jianping Gou"],"abstract":"Deep convolutional neural networks provide a powerful feature learning\ncapability for image classification. The deep image features can be utilized to\ndeal with many image understanding tasks like image classification and object\nrecognition. However, the robustness obtained in one dataset can be hardly\nreproduced in the other domain, which leads to inefficient models far from\nstate-of-the-art. We propose a deep collaborative weight-based classification\n(DeepCWC) method to resolve this problem, by providing a novel option to fully\ntake advantage of deep features in classic machine learning. It firstly\nperforms the L2-norm based collaborative representation on the original images,\nas well as the deep features extracted by deep CNN models. Then, two distance\nvectors, obtained based on the pair of linear representations, are fused\ntogether via a novel collaborative weight. This collaborative weight enables\ndeep and classic representations to weigh each other. We observed the\ncomplementarity between two representations in a series of experiments on 10\nfacial and object datasets. The proposed DeepCWC produces very promising\nclassification results, and outperforms many other benchmark methods,\nespecially the ones claimed for Fashion-MNIST. The code is going to be\npublished in our public repository.","url_abs":"http://arxiv.org/abs/1802.07589v2","url_pdf":"http://arxiv.org/pdf/1802.07589v2.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":"collaboratively-weighting-deep-and-classic","repo_url":"https://github.com/zengsn/research","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","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":"l2-regularization","task_name":"L2 Regularization"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.07589","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}