  Loss function is crucial for model training and feature representation
learning, conventional models usually regard facial attractiveness recognition
task as a regression problem, and adopt MSE loss or Huber variant loss as
supervision to train a deep convolutional neural network (CNN) to predict
facial attractiveness score. Little work has been done to systematically
compare the performance of diverse loss functions. In this paper, we firstly
systematically analyze model performance under diverse loss functions. Then a
novel loss function named ComboLoss is proposed to guide the SEResNeXt50
network. The proposed method achieves state-of-the-art performance on SCUT-FBP,
HotOrNot and SCUT-FBP5500 datasets with an improvement of 1.13%, 2.1% and 0.57%
compared with prior arts, respectively. Code and models are available at
https://github.com/lucasxlu/ComboLoss.git.
