Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
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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.
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