{"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/learning-to-generate-chairs-tables-and-cars","title":"Learning to Generate Chairs, Tables and Cars with Convolutional Networks","arxiv_id":"1411.5928","date":"2014-11-21","proceeding":null,"authors":["Alexey Dosovitskiy","Jost Tobias Springenberg","Maxim Tatarchenko","Thomas Brox"],"abstract":"We train generative 'up-convolutional' neural networks which are able to\ngenerate images of objects given object style, viewpoint, and color. We train\nthe networks on rendered 3D models of chairs, tables, and cars. Our experiments\nshow that the networks do not merely learn all images by heart, but rather find\na meaningful representation of 3D models allowing them to assess the similarity\nof different models, interpolate between given views to generate the missing\nones, extrapolate views, and invent new objects not present in the training set\nby recombining training instances, or even two different object classes.\nMoreover, we show that such generative networks can be used to find\ncorrespondences between different objects from the dataset, outperforming\nexisting approaches on this task.","url_abs":"http://arxiv.org/abs/1411.5928v4","url_pdf":"http://arxiv.org/pdf/1411.5928v4.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":"learning-to-generate-chairs-tables-and-cars","repo_url":"https://github.com/facebookresearch/disentangling-correlated-factors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-to-generate-chairs-tables-and-cars","repo_url":"https://github.com/zo7/deconvfaces","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1411.5928","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}