Title: Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography,
Abstract: Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule
structure inside single cells. Macromolecule classification approaches based on
convolutional neural networks (CNN) were developed to separate millions of
macromolecules captured from ECT systematically. However, given the fast
accumulation of ECT data, it will soon become necessary to use CNN models to
efficiently and accurately separate substantially more macromolecules at the
prediction stage, which requires additional computational costs. To speed up
the prediction, we compress classification models into compact neural networks
with little in accuracy for deployment. Specifically, we propose to perform
model compression through knowledge distillation. Firstly, a complex teacher
network is trained to generate soft labels with better classification
feasibility followed by training of customized student networks with simple
architectures using the soft label to compress model complexity. Our tests
demonstrate that our compressed models significantly reduce the number of
parameters and time cost while maintaining similar classification accuracy.