{"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/fccns-fully-complex-valued-convolutional","title":"FCCNs: Fully Complex-valued Convolutional Networks using Complex-valued Color Model and Loss Function","arxiv_id":null,"date":"2023-01-01","proceeding":"ICCV 2023 1","authors":["Saurabh Yadav","Koteswar Rao Jerripothula"],"abstract":"    Although complex-valued convolutional neural networks (iCNNs) have existed for a while, they lack proper complex-valued image inputs and loss functions. In addition, all their operations are not complex-valued as they have both complex-valued convolutional layers and real-valued fully-connected layers. As a result, they lack an end-to-end flow of complex-valued information, making them inconsistent w.r.t. the claimed operating domain, i.e., complex numbers. Considering these inconsistencies, we propose a complex-valued color model and loss function and turn fully-connected layers into convolutional layers. All these contributions culminate in what we call FCCNs (Fully Complex-valued Convolutional Networks), which take complex-valued images as inputs, perform only complex-valued operations, and have a complex-valued loss function. Thus, our proposed FCCNs have an end-to-end flow of complex-valued information, which lacks in existing iCNNs. Our extensive experiments on five image classification benchmark datasets show that FCCNs consistently perform better than existing iCNNs. Code is available at https://github.com/saurabhya/FCCNs .    ","url_abs":"http://openaccess.thecvf.com//content/ICCV2023/html/Yadav_FCCNs_Fully_Complex-valued_Convolutional_Networks_using_Complex-valued_Color_Model_and_ICCV_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/ICCV2023/papers/Yadav_FCCNs_Fully_Complex-valued_Convolutional_Networks_using_Complex-valued_Color_Model_and_ICCV_2023_paper.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":"fccns-fully-complex-valued-convolutional","repo_url":"https://github.com/saurabhya/fccns","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}