As the development of artificial intelligence， deep neural network has become an essential tool in various pattern recognition tasks. Deploying deep Convolutional Neural Networks （CNN） on edge computing equipment is challenging due to storage space and computing resource constraints. Therefore， deep network compression has become an important research topic in recent years. Low-rank decomposition and vector quantization are the most popular network compression techniques， which both try to find a compact representation of the original network， thereby reducing the redundancy of network parameters. By establishing a joint compression framework， a deep network compression method based on low-rank decomposition and vector decomposition — Quantized Tensor Decomposition （QTD） was proposed to obtain higher compression ratio by performing further quantization based on the low-rank structure of network. Experimental results of classical ResNet and the proposed method on CIFAR-10 dataset show that the volume can be compressed to 1% by QTD with a slight accuracy drop of 1.71 percentage points. Moreover， the proposed method was compared with the quantization-based method PQF （Permute， Quantize， and Fine-tune）， the low-rank decomposition-based method TDNR （Tucker Decomposition with Nonlinear Response）， and the pruning-based method CLIP-Q （Compression Learning by In-parallel Pruning-Quantization） on large dataset ImageNet. Experimental results show that QTD can maintain better classification accuracy with same compression range.
