NettetAlthough the latest high-end smartphone has powerful CPU and GPU, running deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet … Nettet10. mar. 2024 · Compressing CNN Kernels for Videos Using Tucker ... Kim et al. (2016) proposed using a Tucker-decomposition to compress the convolutional kernel of a pre-trained network for images in order to reduce the complexity of the network, i.e. the number of ... The excellent performance of deep neural networks has enabled us ...
Learning Tucker Compression for Deep CNN Semantic Scholar
NettetDECOMPTYPE is either cp (default) or tucker. If a model is already decomposed, it could be passed in as the MODEL parameter (By default, the Torchvision pretrained ResNet50 is used). If continue a fine-tuning from a checkpoint, pass in the checkpoint as CHECKPOINT. To specify the parameters for the model, use STATEDICT. NettetWe explore the effectiveness of combining teacher-student learning and Tucker decomposition. We use teacher-student learning to transfer the knowledge of a large … pest control burbank wa
ADA-Tucker: Compressing Deep Neural Networks via Adaptive
Nettet1. mar. 2024 · Learning Tucker Compression for Deep CNN. Pengyi Hao, Xiaojuan Li, Fuli Wu. Published 1 March 2024. Computer Science. 2024 Data Compression Conference (DCC) Recently, tensor decomposition approaches are used to compress deep convolutional neural networks (CNN) for getting a faster CNN with fewer … NettetIn tensor processing, the most basic methods are canonical polyadic (CP) decomposition and Tucker decomposition. The CP decomposition serves the tensor as a sum of finite … Nettet1. des. 2024 · In this paper, we study teacher-student learning and Tucker decomposition methods to reduce model size and runtime latency for CNN-DBLSTM based character model for OCR. We use teacher-student learning to transfer the knowledge of a large-size teacher model to a small-size compact student model, followed by Tucker … staph pseudintermedius hemolysis