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Although convolutional neural network (CNN) has made great progress, largeredundant parameters restrict its deployment on embedded devices, especiallymobile devices. The recent compression works are focused on real-valueconvolutional neural network (Real CNN), however, to our knowledge, there is noattempt for the compression of complex-value convolutional neural network(Complex CNN). Compared with the real-valued network, the complex-value neuralnetwork is easier to optimize, generalize, and has better learning potential.This paper extends the commonly used deep compression algorithm from realdomain to complex domain and proposes an improved deep compression algorithmfor the compression of Complex CNN. The proposed algorithm compresses thenetwork about 8 times on CIFAR-10 dataset with less than 3% accuracy loss. Onthe ImageNet dataset, our method compresses the model about 16 times and theaccuracy loss is about 2% without retraining.

Compressing complex convolutional neural network based on an improved deep compression algorithm