Iterative Low-Rank Approximation for CNN Compression

23 Mar 2018 Maksym Kholiavchenko

Deep convolutional neural networks contain tens of millions of parameters, making them impossible to work efficiently on embedded devices. We propose iterative approach of applying low-rank approximation to compress deep convolutional neural networks... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Convolution
Convolutions
Darknet-19
Convolutional Neural Networks
Local Response Normalization
Normalization
Grouped Convolution
Convolutions
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Max Pooling
Pooling Operations
Softmax
Output Functions
YOLOv2
Object Detection Models
AlexNet
Convolutional Neural Networks