前言 致力于滤波器的剪枝,论文的方法不改变原始网络的结构.论文的方法是基于下一层的统计信息来进行剪枝,这是区别已有方法的. VGG-16上可以减少3.31FLOPs和16.63倍的压缩,top-5的准确率只下降0.52%.在ResNet-50上可以降低超过一半的参数量和FLOPs,top-5的准确率只降低1%. 如上图所示,在虚线框中找到那些弱通道(weak channels)和他们对应的滤波器(黄色高亮部分),这些通道和对应的滤波器对整体性能贡献较小,因此可以丢弃,这样就得到一个剪枝后的模型,…

论文地址:https://arxiv.org/abs/1707.06342 主要思想 选择一个channel的子集,然后让通过样本以后得到的误差最小(最小二乘),将裁剪问题转换成了优化问题. 这篇论文题目说是对filter的裁剪,其实是对channel的裁剪,对channel裁剪以后,当然涉及filter的裁剪. 对channel裁剪以后当然可以实现压缩和加速. 实现细节 在i+1层中选择channel的子集,因为filter i+1层的个数没有变,所以layer i + 2层的尺寸大小也没有变…

论文<A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding> Pruning by learning only the important connections. all connections with weights below a threshold are removed from the network. retrain the network to learn the…

This example shows how to use Neural Network Toolbox™ to train a deep neural network to classify images of digits. Neural networks with multiple hidden layers can be useful for solving classification problems with complex data, such as images. Each l…

Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds…

A Survey of Model Compression and Acceleration for Deep Neural Network时s 本文全面概述了深度神经网络的压缩方法,主要可分为参数修剪与共享.低秩分解.迁移/压缩卷积滤波器和知识精炼,论文对每一类方法的性能.相关应用.优势和缺陷等方面进行了独到分析. 研究背景 在神经网络方面,早在上个世纪末,Yann LeCun 等人已经使用神经网络成功识别了邮件上的手写邮编.至于深度学习的概念是由 Geoffrey Hinton 等人首次提出…

论文地址:PACDNN:一种用于语音增强的相位感知复合深度神经网络 引用格式:Hasannezhad M,Yu H,Zhu W P,et al. PACDNN: A phase-aware composite deep neural network for speech enhancement[J]. Speech Communication,2022,136:1-13. 摘要 目前,利用深度神经网络(DNN)进行语音增强的大多数方法都面临着一些限制:它们没有利用相位谱中的信息,同时它们的高计算…

XiangBai--[AAAI2017]TextBoxes:A Fast Text Detector with a Single Deep Neural Network 目录 作者和相关链接 方法概括 创新点和贡献 方法细节 实验结果 总结与收获点 作者和相关链接 作者 论文下载 廖明辉,石葆光, 白翔, 王兴刚 ,刘文予 代码下载 方法概括 文章核心: 改进版的SSD用来解决文字检测问题 端到端识别的pipeline: Step 1: 图像输入到修改版SSD网络中 + 非极大值抑制(NMS)→…

The state of the art of non-linearity is to use ReLU instead of sigmoid function in deep neural network, what are the advantages? I know that training a network when ReLU is used would be faster, and it is more biological inspired, what are the other…

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation xx…