CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression
This addresses the challenge of deploying CNNs on low-end smart devices like smartphones, though it appears incremental as it builds on existing compression techniques.
The paper tackles the problem of high memory and computational costs in Convolutional Neural Networks (CNNs) by proposing a compression method using CP-decomposition and Tensor Power Method, achieving significant reductions in memory and computation with no more accuracy loss compared to state-of-the-art methods.
Convolutional Neural Networks (CNNs) has shown a great success in many areas including complex image classification tasks. However, they need a lot of memory and computational cost, which hinders them from running in relatively low-end smart devices such as smart phones. We propose a CNN compression method based on CP-decomposition and Tensor Power Method. We also propose an iterative fine tuning, with which we fine-tune the whole network after decomposing each layer, but before decomposing the next layer. Significant reduction in memory and computation cost is achieved compared to state-of-the-art previous work with no more accuracy loss.