{"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/a-condition-number-for-joint-optimization-of","title":"A Condition Number for Joint Optimization of Cycle-Consistent Networks","arxiv_id":null,"date":"2019-12-01","proceeding":"NeurIPS 2019 12","authors":["Leonidas J. Guibas","Qi-Xing Huang","Zhenxiao Liang"],"abstract":"A recent trend in optimizing maps such as dense correspondences between objects or neural networks between pairs of domains is to optimize them jointly. In this context, there is a natural \\textsl{cycle-consistency} constraint, which regularizes composite maps associated with cycles, i.e., they are forced to be identity maps. However, as there is an exponential number of cycles in a graph, how to sample a subset of cycles becomes critical for efficient and effective enforcement of the cycle-consistency constraint. This paper presents an algorithm that select a subset of weighted cycles to minimize a condition number of the induced joint optimization problem. Experimental results on benchmark datasets justify the effectiveness of our approach for optimizing dense correspondences between 3D shapes and neural networks for predicting dense image flows.","url_abs":"http://papers.nips.cc/paper/8386-a-condition-number-for-joint-optimization-of-cycle-consistent-networks","url_pdf":"http://papers.nips.cc/paper/8386-a-condition-number-for-joint-optimization-of-cycle-consistent-networks.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":"a-condition-number-for-joint-optimization-of","repo_url":"https://github.com/huangqx/NeurIPS19_Cycle","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}