Can I read Fast Data-free Model Compression via Dictionary-pair Reconstruction on EtoBox?

Fast Data-free Model Compression via Dictionary-pair Reconstruction by Yangcheng Gao; Zhao Zhang; Haijun Zhang; Mingbo Zhao; Yi Yang; Meng Wang is a Computer Science article available to read on EtoBox.

What is Fast Data-free Model Compression via Dictionary-pair Reconstruction about?

Deep neural network (DNN) obtained satisfactory results on different vision tasks; however, they usually suffer from large models and massive parameters during model deployment. While DNN compression can reduce the memory footprint of deep model effectively, so that the deep model can be deployed on portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the application that needs fast computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new model compression method, termed dictionary-pair-based fast data-free DNN compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our method performs tensor decomposition of DNN model with a fast dictionary-pair learning-based reconstruction approach, which can be deployed on different weight layers (e.g., convolution and fully connected layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series o

Who reads Fast Data-free Model Compression via Dictionary-pair Reconstruction?

Author

Yangcheng Gao; Zhao Zhang; Haijun Zhang; Mingbo Zhao; Yi Yang; Meng Wang

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)