{"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/spatially-sparse-convolutional-neural","title":"Spatially-sparse convolutional neural networks","arxiv_id":"1409.6070","date":"2014-09-22","proceeding":null,"authors":["Benjamin Graham"],"abstract":"Convolutional neural networks (CNNs) perform well on problems such as\nhandwriting recognition and image classification. However, the performance of\nthe networks is often limited by budget and time constraints, particularly when\ntrying to train deep networks.\n  Motivated by the problem of online handwriting recognition, we developed a\nCNN for processing spatially-sparse inputs; a character drawn with a one-pixel\nwide pen on a high resolution grid looks like a sparse matrix. Taking advantage\nof the sparsity allowed us more efficiently to train and test large, deep CNNs.\nOn the CASIA-OLHWDB1.1 dataset containing 3755 character classes we get a test\nerror of 3.82%.\n  Although pictures are not sparse, they can be thought of as sparse by adding\npadding. Applying a deep convolutional network using sparsity has resulted in a\nsubstantial reduction in test error on the CIFAR small picture datasets: 6.28%\non CIFAR-10 and 24.30% for CIFAR-100.","url_abs":"http://arxiv.org/abs/1409.6070v1","url_pdf":"http://arxiv.org/pdf/1409.6070v1.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":"spatially-sparse-convolutional-neural","repo_url":"https://github.com/facebookresearch/SparseConvNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"spatially-sparse-convolutional-neural","repo_url":"https://github.com/justinessert/hierarchical-deep-cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"spatially-sparse-convolutional-neural","repo_url":"https://github.com/simpleintel/jupyter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"handwriting-recognition","task_name":"Handwriting Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"sparse-convolutions","method_name":"Sparse Convolutions"}],"datasets_introduced":[],"methods_introduced":[{"slug":"sparse-convolutions","name":"Sparse Convolutions","full_name":"Sparse Convolutions"}],"results":[{"leaderboard":"/sota/image-classification-on-cifar-10","task":"Image Classification","dataset":"CIFAR-10","model":"SSCNN","rank_in_archive_order":171,"of":265,"metrics":{"Percentage correct":"93.7"},"uses_additional_data":false},{"leaderboard":"/sota/image-classification-on-cifar-100","task":"Image Classification","dataset":"CIFAR-100","model":"SSCNN","rank_in_archive_order":150,"of":211,"metrics":{"Percentage correct":"75.7"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1409.6070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1409.6070"}},"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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