Knowledge Consistency between Neural Networks and Beyond
Abstract
This paper aims to analyze knowledge consistency between pre-trained deep neural networks. We propose a generic definition for knowledge consistency between neural networks at different fuzziness levels. A task-agnostic method is designed to disentangle feature components, which represent the consistent knowledge, from raw intermediate-layer features of each neural network. As a generic tool, our method can be broadly used for different applications. In preliminary experiments, we have used knowledge consistency as a tool to diagnose representations of neural networks. Knowledge consistency provides new insights to explain the success of existing deep-learning techniques, such as knowledge distillation and network compression. More crucially, knowledge consistency can also be used to refine pre-trained networks and boost performance.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2019
- DOI:
- 10.48550/arXiv.1908.01581
- arXiv:
- arXiv:1908.01581
- Bibcode:
- 2019arXiv190801581L
- Keywords:
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- Computer Science - Machine Learning;
- Computer Science - Computer Vision and Pattern Recognition;
- Statistics - Machine Learning