{"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/manifold-regularized-dynamic-network-pruning","title":"Manifold Regularized Dynamic Network Pruning","arxiv_id":"2103.05861","date":"2021-03-10","proceeding":"CVPR 2021 1","authors":["Yehui Tang","Yunhe Wang","Yixing Xu","Yiping Deng","Chao Xu","DaCheng Tao","Chang Xu"],"abstract":"Neural network pruning is an essential approach for reducing the computational complexity of deep models so that they can be well deployed on resource-limited devices. Compared with conventional methods, the recently developed dynamic pruning methods determine redundant filters variant to each input instance which achieves higher acceleration. Most of the existing methods discover effective sub-networks for each instance independently and do not utilize the relationship between different inputs. To maximally excavate redundancy in the given network architecture, this paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks (dubbed as ManiDP). We first investigate the recognition complexity and feature similarity between images in the training set. Then, the manifold relationship between instances and the pruned sub-networks will be aligned in the training procedure. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost compared to the state-of-the-art methods. For example, our method can reduce 55.3% FLOPs of ResNet-34 with only 0.57% top-1 accuracy degradation on ImageNet.","url_abs":"https://arxiv.org/abs/2103.05861v1","url_pdf":"https://arxiv.org/pdf/2103.05861v1.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":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/2023-MindSpore-1/ms-code-18/tree/main/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/Mind23-2/MindCode-101/tree/main/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/code-implementation1/Code5/tree/main/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/cv/ManiDP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"manifold-regularized-dynamic-network-pruning","repo_url":"https://github.com/yehuitang/Pruning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"network-pruning","task_name":"Network Pruning"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.05861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.05861"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Mind23-2/MindCode-3/tree/main/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Mind23-2/MindCode-101/tree/main/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/2023-MindSpore-1/ms-code-18/tree/main/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/code-implementation1/Code5/tree/main/ManiDP","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yehuitang/Pruning","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/huaweinoah/Pruning","reach":{"status":"unanswered"}}],"summary":{"ran":2},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"8b4a68fca187984d","entry":"MaskBlock","repo":"yehuitang/Pruning","repo_kind":"listed","path":"ManiDP/models/resnet_cifar.py","file_url":"https://github.com/yehuitang/Pruning/blob/HEAD/ManiDP/models/resnet_cifar.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8b4a68fca187984d"}},{"code_sha256_prefix":"addde2254c93026a","entry":"MaskedBasicblock","repo":"yehuitang/Pruning","repo_kind":"listed","path":"ManiDP/models/resnet_cifar.py","file_url":"https://github.com/yehuitang/Pruning/blob/HEAD/ManiDP/models/resnet_cifar.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"addde2254c93026a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}