{"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/igcv3-interleaved-low-rank-group-convolutions","title":"IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks","arxiv_id":"1806.00178","date":"2018-06-01","proceeding":null,"authors":["Ke Sun","Mingjie Li","Dong Liu","Jingdong Wang"],"abstract":"In this paper, we are interested in building lightweight and efficient\nconvolutional neural networks. Inspired by the success of two design patterns,\ncomposition of structured sparse kernels, e.g., interleaved group convolutions\n(IGC), and composition of low-rank kernels, e.g., bottle-neck modules, we study\nthe combination of such two design patterns, using the composition of\nstructured sparse low-rank kernels, to form a convolutional kernel. Rather than\nintroducing a complementary condition over channels, we introduce a loose\ncomplementary condition, which is formulated by imposing the complementary\ncondition over super-channels, to guide the design for generating a dense\nconvolutional kernel. The resulting network is called IGCV3. We empirically\ndemonstrate that the combination of low-rank and sparse kernels boosts the\nperformance and the superiority of our proposed approach to the\nstate-of-the-arts, IGCV2 and MobileNetV2 over image classification on CIFAR and\nImageNet and object detection on COCO.","url_abs":"http://arxiv.org/abs/1806.00178v2","url_pdf":"http://arxiv.org/pdf/1806.00178v2.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":"igcv3-interleaved-low-rank-group-convolutions","repo_url":"https://github.com/homles11/IGCV3","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"igcv3-interleaved-low-rank-group-convolutions","repo_url":"https://github.com/osmr/imgclsmob","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"igcv3-interleaved-low-rank-group-convolutions","repo_url":"https://github.com/xxradon/IGCV3-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"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":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.00178","atlas_url":"https://app.syntology.ai/?focus=1806.00178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.00178"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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