论文标题
通过挤压和兴奋网络进行面部吸引力分析的Comboloss
ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks
论文作者
论文摘要
损失功能对于模型训练和功能表示学习至关重要，常规模型通常将面部吸引力识别任务视为回归问题，并采用MSE损失或Huber变体损失作为培训深度卷积神经网络（CNN）的监督，以预测面部吸引力得分。几乎没有完成系统地比较各种损失功能的性能的工作。在本文中，我们首先系统地分析了不同损失功能下的模型性能。然后提出了一个名为Comboloss的新型损失函数来指导Seresnext50网络。所提出的方法在Scut-FBP，Hotornot和Scut-FBP5500数据集方面取得了最先进的性能，与先前的艺术相比分别提高了1.13％，2.1％和0.57％。代码和型号可在https://github.com/lucasxlu/comboloss.git上找到。
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.
