{"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/inception-recurrent-convolutional-neural","title":"Inception Recurrent Convolutional Neural Network for Object Recognition","arxiv_id":"1704.07709","date":"2017-04-25","proceeding":"CVPR 2015","authors":["Md Zahangir Alom","Mahmudul Hasan","Chris Yakopcic","Tarek M. Taha"],"abstract":"Deep convolutional neural networks (DCNNs) are an influential tool for\nsolving various problems in the machine learning and computer vision fields. In\nthis paper, we introduce a new deep learning model called an Inception-\nRecurrent Convolutional Neural Network (IRCNN), which utilizes the power of an\ninception network combined with recurrent layers in DCNN architecture. We have\nempirically evaluated the recognition performance of the proposed IRCNN model\nusing different benchmark datasets such as MNIST, CIFAR-10, CIFAR- 100, and\nSVHN. Experimental results show similar or higher recognition accuracy when\ncompared to most of the popular DCNNs including the RCNN. Furthermore, we have\ninvestigated IRCNN performance against equivalent Inception Networks and\nInception-Residual Networks using the CIFAR-100 dataset. We report about 3.5%,\n3.47% and 2.54% improvement in classification accuracy when compared to the\nRCNN, equivalent Inception Networks, and Inception- Residual Networks on the\naugmented CIFAR- 100 dataset respectively.","url_abs":"http://arxiv.org/abs/1704.07709v1","url_pdf":"http://arxiv.org/pdf/1704.07709v1.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":"inception-recurrent-convolutional-neural","repo_url":"https://github.com/Insiyaa/IRCNN-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[{"method_slug":"dcnn","method_name":"DCNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.07709","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}