This paper studies teacher-student optimization on neural networks, i.e., adopting the supervision from a trained (teacher) network to optimize another (student) network. Conventional approaches enforced the student to learn from a strict teacher which fit a hard distribution and achieved high recognition accuracy, but we argue that a more tolerant teacher often educate better students. We start with adding an extra loss term to a patriarch network so that it preserves confidence scores on a primary class (the ground-truth) and several visually-similar secondary classes. The patriarch is also known as the first teacher. In each of the following generations, a student learns from the teacher and becomes the new teacher in the next generation. Although the patriarch is less powerful due to ambiguity, the students enjoy a persistent ability growth as we gradually fine-tune them to fit one-hot distributions. We investigate standard image classification tasks (CIFAR100 and ILSVRC2012). Experiments with different network architectures verify the superiority of our approach, either using a single model or an ensemble of models.

本文从严格性控制角度研究了深度神经网络的教师网络和学生网络的优化问题。通过针对教师网络训练中的宽松性进行优化，使得学生网络在学习过程中更容易获得跨类别相似性信息，从而在CIFAR100和ILSVRC2012等数据集的图像分类任务中取得更高的分类准确率。

代际知识蒸馏：更具宽容性的教师教育更好的学生