arXiv:1701.07148
CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
토론
이 논문과 연결된 포럼 글이 아직 없습니다.