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Age estimation of facial images is very challenging because of the complexity of face aging process and the difficulty of collecting and labeling data. A holistic regression model is subject to imbalanced training data, while a divide-and-conquer method highly depends on the effect of the age classification, which usually has boundary effect due to cross-age correlations. This paper proposes a simple but effective multi-task learning (MTL) network combining classification and regression for agedoi:10.1109/access.2020.2994322 fatcat:zpssb4nn4bbd3ijk6llajf2qhm