{"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/unsupervised-learning-with-sparse-space-and","title":"Unsupervised learning with sparse space-and-time autoencoders","arxiv_id":"1811.10355","date":"2018-11-26","proceeding":null,"authors":["Benjamin Graham"],"abstract":"We use spatially-sparse two, three and four dimensional convolutional\nautoencoder networks to model sparse structures in 2D space, 3D space, and\n3+1=4 dimensional space-time. We evaluate the resulting latent spaces by\ntesting their usefulness for downstream tasks. Applications are to handwriting\nrecognition in 2D, segmentation for parts in 3D objects, segmentation for\nobjects in 3D scenes, and body-part segmentation for 4D wire-frame models\ngenerated from motion capture data.","url_abs":"http://arxiv.org/abs/1811.10355v1","url_pdf":"http://arxiv.org/pdf/1811.10355v1.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":"unsupervised-learning-with-sparse-space-and","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"}}],"tasks":[{"task_slug":"handwriting-recognition","task_name":"Handwriting Recognition"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}