Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
This addresses the challenge of input efficiency for deep learning models, particularly in resource-constrained domains like speech recognition, but it is incremental as it builds on existing knowledge distillation concepts.
The paper tackles the problem of reducing input dimensions for deep neural networks by proposing progressive label distillation, which uses teacher-student pairs to generate distilled training data, and reports a test accuracy increase of almost 78% compared to direct learning in a speech recognition task.
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.