Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks

  • Zhong Qiu Lin
  • Alexander Wong

Abstract

Much of the focus in the area of knowledge distillation has been
on distilling knowledge from a larger teacher network to a smaller
student network. However, there has been little research on how
the concept of distillation can be leveraged to distill the knowledge
encapsulated in the training data itself into a reduced form. In this
study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate the efficacy of the proposed progressive label distillation approach, we experimented with learning a deep limited vocabulary speech recognition network based on generated 500ms input utterances distilled progressively from 1000ms source training data, and demonstrated a significant increase in test accuracy of almost 78% compared to direct learning.

Published
2020-01-02
How to Cite
Lin, Z., & Wong, A. (2020). Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks. Journal of Computational Vision and Imaging Systems, 5(1), 1. Retrieved from https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1666