《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》 期刊:TPAMI 核心思想: 存储编码器最大池化层中最大值的索引,上采样时,将特征图根据存储的索引对其恢复,再对其卷积。大幅度减少计算量。 引言 作者提出架构SegNet被设计成一种用于像素语义分割的高效架构
Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation 截至3.8,引用次数22 这篇文章把CT腹部扫描图分割当成一个分类问题处理,使用一个基于CNN的级连分类器框架。使用两个编码解码器卷积网络训练来进行级联分割肝和病灶(EDCNN)。即第一个EDCNN分割肝图片的结果(ROI区域)作...
Product Description The LogiCORE™ IP Convolutional Encoder core can be used in a wide variety of error correcting applications and is typically used in conjunction with the Viterbi Decoder. The core is parameterizable, allowing the designer to control the constraint length and the type of convol...
decode网络中的decoder 利用对应encoder feature map中保存的max-index对输入的feature map进行上采样,产生的稀疏feature maps后接一系列可训练的卷积核,输出密集的feature maps,后接BN用于规范化处理正则化减弱过拟合,与输入对应的decoder产生多通道feature map,虽然输入只有(RGB)三通道。其他的encoder,decoder的通道数,...
使用了对称的Encoder-Decoder网络结构来实现语义分割。Encoder编码器处执行卷积和最大池化,在进行最大池化时,存储相应的最大池化索引(位置);Decoder解码器执行上采样和卷积,最后将每个像素送到softmax分类器,其中在上采样期间,调用相应编码器层处的最大池化索引以进行上采样; ...
This shifts the input data to be encoded by at least one bit position relative to each other, on the basis of a desired encoding rate, for processing by an encoder module (FE1,FE2). A point encoder in the form of a logic combining circuit processes the output data of the encoder ...
In this paper, we propose a convolutional neural network, which is based on down sampling followed by up sampling architecture for the purpose of road extraction from aerial images. Our model consists of convolutional layers only. The proposed encoder-decoder structure allows our network to retain ...
In the decoder, dummy bits are inserted in place of the omitted bits and the Viterbi algorithm is carried out as described. For example, in OFDM IEEE 802.11 a K = 7 convolutional encoder can produce code rates of ½ (basic), 2/3 or ¾, plus 5/6 for the high-throughput and very...
网络卷积编码器;回旋编码器;乘积编码器 网络释义
This is essentially a Convolutional Encoder Decoder network based on the SegNet architecture for unsupervised feature learning. The particular network can be used for unsupervised feature learning on particular datasets, as well as then fine-tune (further train) the pre-trained network for semantic seg...