{"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/on-complex-valued-convolutional-neural","title":"On Complex Valued Convolutional Neural Networks","arxiv_id":"1602.09046","date":"2016-02-29","proceeding":null,"authors":["Nitzan Guberman"],"abstract":"Convolutional neural networks (CNNs) are the cutting edge model for\nsupervised machine learning in computer vision. In recent years CNNs have\noutperformed traditional approaches in many computer vision tasks such as\nobject detection, image classification and face recognition. CNNs are\nvulnerable to overfitting, and a lot of research focuses on finding\nregularization methods to overcome it. One approach is designing task specific\nmodels based on prior knowledge.\n  Several works have shown that properties of natural images can be easily\ncaptured using complex numbers. Motivated by these works, we present a\nvariation of the CNN model with complex valued input and weights. We construct\nthe complex model as a generalization of the real model. Lack of order over the\ncomplex field raises several difficulties both in the definition and in the\ntraining of the network. We address these issues and suggest possible\nsolutions.\n  The resulting model is shown to be a restricted form of a real valued CNN\nwith twice the parameters. It is sensitive to phase structure, and we suggest\nit serves as a regularized model for problems where such structure is\nimportant. This suggestion is verified empirically by comparing the performance\nof a complex and a real network in the problem of cell detection. The two\nnetworks achieve comparable results, and although the complex model is hard to\ntrain, it is significantly less vulnerable to overfitting. We also demonstrate\nthat the complex network detects meaningful phase structure in the data.","url_abs":"http://arxiv.org/abs/1602.09046v1","url_pdf":"http://arxiv.org/pdf/1602.09046v1.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":"on-complex-valued-convolutional-neural","repo_url":"https://github.com/Doyosae/Deep-Complex-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"on-complex-valued-convolutional-neural","repo_url":"https://github.com/Doyosae/Deep_Complex_Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"on-complex-valued-convolutional-neural","repo_url":"https://github.com/omrijsharon/torchlex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"on-complex-valued-convolutional-neural","repo_url":"https://github.com/ypeleg/komplex","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"on-complex-valued-convolutional-neural","repo_url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"cell-detection","task_name":"Cell Detection"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}