Convolution neural network is successful in pervasive vision tasks, including
label distribution learning, which usually takes the form of learning an
injection from the non-linear visual features to the well-defined labels.
However, how the discrepancy between features is mapped to the label
discrepancy is ambient, and its correctness is not guaranteed.To address these
problems, we study the mathematical connection between feature and its label,
presenting a general and simple framework for label distribution learning. We
propose a so-called Triangular Distribution Transform (TDT) to build an
injective function between feature and label, guaranteeing that any symmetric
feature discrepancy linearly reflects the difference between labels. The
proposed TDT can be used as a plug-in in mainstream backbone networks to
address different label distribution learning tasks. Experiments on Facial Age
Recognition, Illumination Chromaticity Estimation, and Aesthetics assessment
show that TDT achieves on-par or better results than the prior arts.

卷积神经网络在广泛的视觉任务中取得成功，本文研究了特征和标签之间的数学连接，提出了一种用于标签分布学习的通用且简单的框架，通过提出的三角分布变换（TDT）在主流的骨干网络中实现不同的标签分布学习任务。实验证明，TDT 在人脸年龄识别、照明色度估计和美学评估等方面取得了与先前研究相当甚至更好的结果。