A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers

ECCV 2018 • Tianyun Zhang • Shaokai Ye • Kaiqi Zhang • Jian Tang • Wujie Wen • Makan Fardad • Yanzhi Wang

Weight pruning methods for deep neural networks (DNNs) have been investigated recently, but prior work in this area is mainly heuristic, iterative pruning, thereby lacking guarantees on the weight reduction ratio and convergence time. To mitigate these limitations, we present a systematic weight pruning framework of DNNs using the alternating direction method of multipliers (ADMM)... (read more)

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METHOD	TYPE
1x1 Convolution	Convolutions
Convolution	Convolutions
Local Response Normalization	Normalization
Grouped Convolution	Convolutions
ReLU	Activation Functions
Dropout	Regularization
Dense Connections	Feedforward Networks
Max Pooling	Pooling Operations
Softmax	Output Functions
AlexNet	Convolutional Neural Networks
ADMM	Optimization

METHOD

TYPE

1x1 Convolution