{"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/deep-unsupervised-similarity-learning-using","title":"Deep Unsupervised Similarity Learning using Partially Ordered Sets","arxiv_id":"1704.02268","date":"2017-04-07","proceeding":"CVPR 2017 7","authors":["Miguel A. Bautista","Artsiom Sanakoyeu","Björn Ommer"],"abstract":"Unsupervised learning of visual similarities is of paramount importance to\ncomputer vision, particularly due to lacking training data for fine-grained\nsimilarities. Deep learning of similarities is often based on relationships\nbetween pairs or triplets of samples. Many of these relations are unreliable\nand mutually contradicting, implying inconsistencies when trained without\nsupervision information that relates different tuples or triplets to each\nother. To overcome this problem, we use local estimates of reliable\n(dis-)similarities to initially group samples into compact surrogate classes\nand use local partial orders of samples to classes to link classes to each\nother. Similarity learning is then formulated as a partial ordering task with\nsoft correspondences of all samples to classes. Adopting a strategy of\nself-supervision, a CNN is trained to optimally represent samples in a mutually\nconsistent manner while updating the classes. The similarity learning and\ngrouping procedure are integrated in a single model and optimized jointly. The\nproposed unsupervised approach shows competitive performance on detailed pose\nestimation and object classification.","url_abs":"http://arxiv.org/abs/1704.02268v3","url_pdf":"http://arxiv.org/pdf/1704.02268v3.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":"deep-unsupervised-similarity-learning-using","repo_url":"https://github.com/asanakoy/deeppose_tf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-unsupervised-similarity-learning-using","repo_url":"https://github.com/asanakoy/deep_unsupervised_posets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.02268","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}