{"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/mean-variance-loss-for-deep-age-estimation","title":"Mean-Variance Loss for Deep Age Estimation From a Face","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Hongyu Pan","Hu Han","Shiguang Shan","Xilin Chen"],"abstract":"Age estimation has broad application prospects of many fields, such as video surveillance, social networking, and human-computer interaction. However, many of the published age estimation approaches simply treat the age estimation as an exact age regression problem, and thus did not leverage a distribution's robustness in representing labels with ambiguity such as ages. In this paper, we propose a new loss function, called mean-variance loss, for robust age estimation via distribution learning. Specifically, the mean-variance loss consists of a mean loss, which penalizes difference between the mean of the estimated age distribution and the ground-truth age, and a variance loss, which penalizes the variance of the estimated age distribution to ensure a concentrated distribution. The proposed mean-variance loss and softmax loss are embedded jointly into Convolutional Neural Networks (CNNs) for age estimation, and the network weights are optimized via stochastic gradient descent (SGD) in an end-to-end learning way. Experimental results on a number of challenging face aging databases (FG-NET, MORPH Album II, and CLAP2016) show that the proposed approach outperforms the state-of-the-art methods by a large margin using a single model.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Pan_Mean-Variance_Loss_for_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Pan_Mean-Variance_Loss_for_CVPR_2018_paper.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":[],"tasks":[{"task_slug":"age-estimation","task_name":"Age Estimation"},{"task_slug":"morph","task_name":"MORPH"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-estimation-on-chalearn-2016","task":"Age Estimation","dataset":"ChaLearn 2016","model":"Mean-Variance","rank_in_archive_order":3,"of":4,"metrics":{"e-error":"0.2867"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}