Universal Deep Neural Network Compression

7 Feb 2018 • Yoojin Choi • Mostafa El-Khamy • Jungwon Lee

In this paper, we investigate lossy compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment. Whereas the previous work addressed non-universal scalar quantization and entropy coding of DNN weights, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding... (read more)

PDF Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Average Pooling	Pooling Operations
ReLU	Activation Functions
1x1 Convolution	Convolutions
Batch Normalization	Normalization
Bottleneck Residual Block	Skip Connection Blocks
Global Average Pooling	Pooling Operations
Residual Block	Skip Connection Blocks
Kaiming Initialization	Initialization
Max Pooling	Pooling Operations
Residual Connection	Skip Connections
Convolution	Convolutions
ResNet	Convolutional Neural Networks

METHOD

TYPE

Average Pooling