{"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/towards-conceptual-compression","title":"Towards Conceptual Compression","arxiv_id":"1604.08772","date":"2016-04-29","proceeding":"NeurIPS 2016 12","authors":["Karol Gregor","Frederic Besse","Danilo Jimenez Rezende","Ivo Danihelka","Daan Wierstra"],"abstract":"We introduce a simple recurrent variational auto-encoder architecture that\nsignificantly improves image modeling. The system represents the\nstate-of-the-art in latent variable models for both the ImageNet and Omniglot\ndatasets. We show that it naturally separates global conceptual information\nfrom lower level details, thus addressing one of the fundamentally desired\nproperties of unsupervised learning. Furthermore, the possibility of\nrestricting ourselves to storing only global information about an image allows\nus to achieve high quality 'conceptual compression'.","url_abs":"http://arxiv.org/abs/1604.08772v1","url_pdf":"http://arxiv.org/pdf/1604.08772v1.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":"towards-conceptual-compression","repo_url":"https://github.com/musyoku/convolutional-draw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.08772","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}