  Automated facial age estimation has diverse real-world applications in
multimedia analysis, e.g., video surveillance, and human-computer interaction.
However, due to the randomness and ambiguity of the aging process, age
assessment is challenging. Most research work over the topic regards the task
as one of age regression, classification, and ranking problems, and cannot well
leverage age distribution in representing labels with age ambiguity. In this
work, we propose a simple yet effective loss function for robust facial age
estimation via distribution learning, i.e., adaptive mean-residue loss, in
which, the mean loss penalizes the difference between the estimated age
distribution's mean and the ground-truth age, whereas the residue loss
penalizes the entropy of age probability out of dynamic top-K in the
distribution. Experimental results in the datasets FG-NET and CLAP2016 have
validated the effectiveness of the proposed loss. Our code is available at
https://github.com/jacobzhaoziyuan/AMR-Loss.
