  The modern convolutional neural networks although achieve great results in
solving complex computer vision tasks still cannot be effectively used in
mobile and embedded devices due to the strict requirements for computational
complexity, memory and power consumption. The CNNs have to be compressed and
accelerated before deployment. In order to solve this problem the novel
approach combining two known methods, low-rank tensor approximation in Tucker
format and quantization of weights and feature maps (activations), is proposed.
The greedy one-step and multi-step algorithms for the task of multilinear rank
selection are proposed. The approach for quality restoration after applying
Tucker decomposition and quantization is developed. The efficiency of our
method is demonstrated for ResNet18 and ResNet34 on CIFAR-10, CIFAR-100 and
Imagenet classification tasks. As a result of comparative analysis performed
for other methods for compression and acceleration our approach showed its
promising features.
