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 ...
两派 1. 新的卷机计算方法 这种是直接提出新的卷机计算方式,从而减少参数,达到压缩模型的效果,例如SqueezedNet,mobileNet SqueezeNet: AlexNet-level accuracy with 50x fewer parameters ...
论文地址:面向基于深度学习的语音增强模型压缩 论文代码:没开源,鼓励大家去向作者要呀,作者是中国人,在语音增强领域 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,深度学习已成为机器学习中最主流的分支之一。它的广泛应用不计其数,无需多言。但众所周知深度神经网络(DNN)有个很大的缺点就是计算量太大。这很大程度上阻碍了基于深度学习方法的产品化,尤其是在一些边缘设备上。因为边缘设备大多不是为计算密集任务设计的,如果简单部署上去则功耗、时延 ...
GAN Compression: Efficient Architectures for Interactive Conditional GANs Abstract ...
模型压缩经典的论文总结于此,方便以后查找!!! Survey Recent Advances in Efficient Computation of Deep Convolutional Neural Networks, [arxiv '18] A Survey of Model ...
论文:Lin M, Chen Q, Yan S. Network In Network[J]. Computer Science, 2013. 参考:关于CNN中1×1卷积核和Network in Network的理解 参考: 深度学习(二十六)Network In Network ...
一、方法总结 Network Pruning Knowledge Distillation Parameter Quantization Architecture Design Dynamic Computation 二、Network Pruning 模型通常是 ...