# Compressing complex convolutional neural network based on an improved deep compression algorithm Source: [Compressing complex convolutional neural network based on an improved deep compression algorithm]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:27:45.299Z Authors: Jiasong Wu, Hongshan Ren, Youyong Kong, Chunfeng Yang, Lotfi Senhadji, Huazhong Shu Published: 2019-03-06T13:20:36.000Z ## Abstract Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices. The recent compression works are focused on real-value convolutional neural network (Real CNN), however, to our knowledge, there is no attempt for the compression of complex-value convolutional neural network (Complex CNN). Compared with the real-valued network, the complex-value neural network is easier to optimize, generalize, and has better learning potential. This paper extends the commonly used deep compression algorithm from real domain to complex domain and proposes an improved deep compression algorithm for the compression of Complex CNN. The proposed algorithm compresses the network about 8 times on CIFAR-10 dataset with less than 3% accuracy loss. On the ImageNet dataset, our method compresses the model about 16 times and the accuracy loss is about 2% without retraining. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()