Deep Neural Network Compression by In-Parallel Pruning-Quantization 论文笔记 摘要 深度神经网络在视觉识别任务(如图像分类和物体检测)上实现了最先进的精确度。然而,现代网络包含数百万个已学习的连接,并且当前的趋势是朝向更深和更密集连接的体系结构。这对在资源受限的系统(例如智能手机或移动机器人)上部署最先进...
深度压缩 DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING:用剪枝、训练量化和霍夫曼编码压缩深度神经网络 这篇论文是Stanford的Son
Disclosed is a compression method for a deep neural network. The deep neural network comprises a plurality of layers. The method includes the following steps for each of at least one layer of the plurality of layers other than an input layer: reading parameters of that layer from a parameter...
作者简介:帆哥,云从研究院深度学习研究团队成员。主攻网络优化与加速 本次介绍的方法为“深度压缩”,文章来自2016ICLR最佳论文 《Deep Compression: Compression Deep Neural Networks With Pruning, Trained Q…
Interpreting Convolutional Neural Networks Through Compression In our compression, the filter importance index is defined as the classification accuracy reduction (CAR) of the network after pruning that filter. The filters are then ...
The computation and storage capacity of the edge device are limited, which seriously restrict the application of deep neural network in the device. Toward to the intelligent application of the edge device, we introduce the deep neural network compression algorithm based on knowledge transfer, a three...
比较经典的是《DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING》[10]这篇论文,其中用到了权重共享的方法 例如2.09,2.12,1.92,1.97这些权重可以当成一个数字2,我们把这些权重聚类,用字典保存,然后进行编码这些权重,最后重新训练权重共享过的模型。
DEEP COMPRESSION小记 2016ICLR最佳论文 Deep Compression: Compression Deep Neural Networks With Pruning, Trained Quantization And Huffman Codin 主要针对神经网络模型巨大,在嵌入式机器中比较难运行的问题。 abstruct 压缩网络包括三个阶段:pruning, trained quantization and Huffman coding,能将模型...
Deep Compression ,three stage pipeline:pruning,trained quantization,Huffman coding reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Steps 1、prun the network by learning only the important connections. ...
II. SYSTEM ARCHITECTURE OF NEURAL NETWORK BASED VIDEO COMPRESSION: 本节介绍我们的深度视频编码器的系统结构,如图1所示。利用帧内相关或帧间相关通过预测编码器形成图像块的紧凑表征预测,并利用帧间/帧内残差网络对残差进行压缩。预测系数和残差系数都经过量化和熵编码,生成最终的二进制流。如图1所示,整个编码系统包...