{"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/early-visual-concept-learning-with","title":"Early Visual Concept Learning with Unsupervised Deep Learning","arxiv_id":"1606.05579","date":"2016-06-17","proceeding":null,"authors":["Irina Higgins","Loic Matthey","Xavier Glorot","Arka Pal","Benigno Uria","Charles Blundell","Shakir Mohamed","Alexander Lerchner"],"abstract":"Automated discovery of early visual concepts from raw image data is a major\nopen challenge in AI research. Addressing this problem, we propose an\nunsupervised approach for learning disentangled representations of the\nunderlying factors of variation. We draw inspiration from neuroscience, and\nshow how this can be achieved in an unsupervised generative model by applying\nthe same learning pressures as have been suggested to act in the ventral visual\nstream in the brain. By enforcing redundancy reduction, encouraging statistical\nindependence, and exposure to data with transform continuities analogous to\nthose to which human infants are exposed, we obtain a variational autoencoder\n(VAE) framework capable of learning disentangled factors. Our approach makes\nfew assumptions and works well across a wide variety of datasets. Furthermore,\nour solution has useful emergent properties, such as zero-shot inference and an\nintuitive understanding of \"objectness\".","url_abs":"http://arxiv.org/abs/1606.05579v3","url_pdf":"http://arxiv.org/pdf/1606.05579v3.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":"early-visual-concept-learning-with","repo_url":"https://github.com/takuseno/beta-vae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.05579","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}