{"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/compression-of-deep-convolutional-neural","title":"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications","arxiv_id":"1511.06530","date":"2015-11-20","proceeding":null,"authors":["Yong-Deok Kim","Eunhyeok Park","Sungjoo Yoo","Taelim Choi","Lu Yang","Dongjun Shin"],"abstract":"Although the latest high-end smartphone has powerful CPU and GPU, running\ndeeper convolutional neural networks (CNNs) for complex tasks such as ImageNet\nclassification on mobile devices is challenging. To deploy deep CNNs on mobile\ndevices, we present a simple and effective scheme to compress the entire CNN,\nwhich we call one-shot whole network compression. The proposed scheme consists\nof three steps: (1) rank selection with variational Bayesian matrix\nfactorization, (2) Tucker decomposition on kernel tensor, and (3) fine-tuning\nto recover accumulated loss of accuracy, and each step can be easily\nimplemented using publicly available tools. We demonstrate the effectiveness of\nthe proposed scheme by testing the performance of various compressed CNNs\n(AlexNet, VGGS, GoogLeNet, and VGG-16) on the smartphone. Significant\nreductions in model size, runtime, and energy consumption are obtained, at the\ncost of small loss in accuracy. In addition, we address the important\nimplementation level issue on 1?1 convolution, which is a key operation of\ninception module of GoogLeNet as well as CNNs compressed by our proposed\nscheme.","url_abs":"http://arxiv.org/abs/1511.06530v2","url_pdf":"http://arxiv.org/pdf/1511.06530v2.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":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/Graphiiz/low-rank-factorization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/Oliver-jiang/Tensor-decomposition-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/TaehyeonKim-pyomu/CNN_compression_rank_selection_BayesOpt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/jacobgil/pytorch-tensor-decompositions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/keithyuck/Object-Tracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/larry0123du/Decompose-CNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"compression-of-deep-convolutional-neural","repo_url":"https://github.com/mostafaelhoushi/tensor-decompositions","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"auxiliary-classifier","method_name":"Auxiliary Classifier"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"googlenet","method_name":"GoogLeNet"},{"method_slug":"inception-module","method_name":"Inception Module"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.06530","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1511.06530"}},"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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