{"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/convolutional-neural-networks-with-1","title":"Convolutional Neural Networks with Alternately Updated Clique","arxiv_id":"1802.10419","date":"2018-02-28","proceeding":"CVPR 2018 6","authors":["Yibo Yang","Zhisheng Zhong","Tiancheng Shen","Zhouchen Lin"],"abstract":"Improving information flow in deep networks helps to ease the training\ndifficulties and utilize parameters more efficiently. Here we propose a new\nconvolutional neural network architecture with alternately updated clique\n(CliqueNet). In contrast to prior networks, there are both forward and backward\nconnections between any two layers in the same block. The layers are\nconstructed as a loop and are updated alternately. The CliqueNet has some\nunique properties. For each layer, it is both the input and output of any other\nlayer in the same block, so that the information flow among layers is\nmaximized. During propagation, the newly updated layers are concatenated to\nre-update previously updated layer, and parameters are reused for multiple\ntimes. This recurrent feedback structure is able to bring higher level visual\ninformation back to refine low-level filters and achieve spatial attention. We\nanalyze the features generated at different stages and observe that using\nrefined features leads to a better result. We adopt a multi-scale feature\nstrategy that effectively avoids the progressive growth of parameters.\nExperiments on image recognition datasets including CIFAR-10, CIFAR-100, SVHN\nand ImageNet show that our proposed models achieve the state-of-the-art\nperformance with fewer parameters.","url_abs":"http://arxiv.org/abs/1802.10419v3","url_pdf":"http://arxiv.org/pdf/1802.10419v3.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":"convolutional-neural-networks-with-1","repo_url":"https://github.com/iboing/CliqueNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"convolutional-neural-networks-with-1","repo_url":"https://github.com/lishuliang/Emotion-Recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"convolutional-neural-networks-with-1","repo_url":"https://github.com/yaowenlong/clique","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10419"}},"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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