Kernel-wise difference minimization for convolutional neural network compression in metaverse - Chang, Yi-Ting (Frontiers in Big Data, 2023)
Convolutional neural networks have achieved remarkable success in computer vision research. However, to further improve their performance, network models have become increasingly complex and require more memory and computational resources. As a result, model compression has become an essential area of research in recent years. In this study, we focus on the best-case scenario for Huffman coding, which involves data with lower entropy. Building on this concept, we formulate a compression with a filter-wise difference minimization problem and propose a novel algorithm to solve it. Our approach involves filter-level pruning, followed by minimizing the difference between filters. Additionally, we perform filter permutation to further enhance compression. Our proposed algorithm achieves a compression rate of 94× on Lenet-5 and 50× on VGG16. The results demonstrate the effectiveness of our method in significantly reducing the size of deep neural networks while maintaining a high level of accuracy. We believe that our approach holds great promise in advancing the field of model compression and can benefit various applications that require efficient neural network models. Overall, this study provides important insights and contributions toward addressing the challenges of model compression in deep neural networks.