WebIn this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning … Web7 apr. 2024 · Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for …
Literature Review of Deep Network Compression
WebAbstract The use of deep learning has grown increasingly in recent years, thereby becoming a much-discussed topic across a diverse range of fields, especially in computer vision, text mining, and speech recognition. Deep learning methods have proven to be robust in representation learning and attained extrao... Full description Description WebIn this thesis, we explore network compression and neural architecture search to design efficient deep learning models. Specifically, we aim at addressing several common … sharonne shimai
Deep neural network compression by Tucker decomposition
Web1 okt. 2015 · Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han, Huizi Mao, William J. Dally Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. Webthe convolutional layers of deep neural networks. Our re-sults show that our TR-Nets approach is able to compress LeNet-5 by 11×without losing accuracy, and can compress the state-of-the-art Wide ResNet by 243×with only 2.3% degradation in Cifar10 image classification. Overall, this compression scheme shows promise in scientific comput- Web1 feb. 2024 · The literature abounds with thorough reviews of compression methods for NNs: the interested reader can refer for instance to [16], [17]. ... Reproducing the sparse … sharon nester