학술논문
Pruning convolution neural network (squeezenet) using taylor expansion-based criterion
Document Type
Conference
Author
Source
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) Signal Processing and Information Technology (ISSPIT), 2018 IEEE International Symposium on. :1-5 Dec, 2018
Subject
Language
Abstract
Recent research in the field of deep learning focuses on reducing the model size of the Convolution Neural Network (CNN) by various compression techniques like Pruning, Quantization and Encoding (eg. Huffman encoding). This paper proposes a way to prune the CNN based on Taylor expansion of change in cost function ΔC of the model. The proposed algorithm uses greedy criteria based pruning with fine-tuning by backpropagation on SqueezeNet architecture. Transfer learning technique is used to train the SqueezeNet on the CIFAR-10 dataset. The proposed algorithm achieves 70% model reduction on SqueezeNet architecture with only 1% drop in accuracy.