Convolutional Neural Networks ImageNet Models Architecture Design Activation Functions Vis ...
. Parameter pruning and sharing . Quantization and Binarization Compressing deep convolutional networks using vector quantization Quantized convolutional neural networks for mobile devices Improving ...
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Convolutional Neural Networks ImageNet Models Architecture Design Activation Functions Vis ...
兩派 1. 新的卷機計算方法 這種是直接提出新的卷機計算方式,從而減少參數,達到壓縮模型的效果,例如SqueezedNet,mobileNet SqueezeNet: AlexNet-level accuracy with 50x fewer parameters ...
論文地址:面向基於深度學習的語音增強模型壓縮 論文代碼:沒開源,鼓勵大家去向作者要呀,作者是中國人,在語音增強領域 深耕多年 引用格式:Tan K, Wang D L. Towards model compression for deep learning based speech ...
1. 背景 今天,深度學習已成為機器學習中最主流的分支之一。它的廣泛應用不計其數,無需多言。但眾所周知深度神經網絡(DNN)有個很大的缺點就是計算量太大。這很大程度上阻礙了基於深度學習方法的產品化,尤其是在一些邊緣設備上。因為邊緣設備大多不是為計算密集任務設計的,如果簡單部署上去則功耗、時延 ...
GAN Compression: Efficient Architectures for Interactive Conditional GANs Abstract ...
模型壓縮經典的論文總結於此,方便以后查找!!! Survey Recent Advances in Efficient Computation of Deep Convolutional Neural Networks, [arxiv '18] A Survey of Model ...
論文:Lin M, Chen Q, Yan S. Network In Network[J]. Computer Science, 2013. 參考:關於CNN中1×1卷積核和Network in Network的理解 參考: 深度學習(二十六)Network In Network ...
一、方法總結 Network Pruning Knowledge Distillation Parameter Quantization Architecture Design Dynamic Computation 二、Network Pruning 模型通常是 ...