{"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/agenet-deeply-learned-regressor-and","title":"AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation","arxiv_id":null,"date":"2015-11-13","proceeding":"ICCV Workshop 2015 11","authors":["Xin Liu","Shaoxin Li","Meina Kan","Jie Zhang","Shuzhe Wu","Wenxian Liu","Hu Han","Shiguang Shan","Xilin Chen"],"abstract":"Apparent age estimation from face image has attracted\r\nmore and more attentions as it is favorable in some realworld applications. In this work, we propose an end-toend learning approach for robust apparent age estimation,\r\nnamed by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models,\r\ni.e., real-value based regression models and Gaussian label distribution based classification models. For both kind\r\nof models, large-scale deep convolutional neural network is\r\nadopted to learn informative age representations. Another\r\nkey feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale webcollected face dataset with identity label, and then it is finetuned on a large-scale real age dataset with noisy age label.\r\nFinally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn\r\n2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent\r\nage estimation.","url_abs":"https://openaccess.thecvf.com/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_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":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/age-estimation-on-chalearn-2015","task":"Age Estimation","dataset":"ChaLearn 2015","model":"AgeNet","rank_in_archive_order":4,"of":7,"metrics":{"e-error":"0.270685"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}