HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs CVPR2019的论文作者来自印度坎普尔理工学院,QS150左右,跟国内的哈尔滨工业大学差不多,这篇论是用一个卷积方法,在不损失准确度的前提下,提高效率。新颖程度不高,可解释性不是很强,至于性能提高的理由作者并没有给出很充分了证明。但是,其方法简单,可...
Convolution neural networkSpatially-local correlationPoint cloud is accepted as an adequate representation for 3D data and most 3D sensors have the ability to generate this data. Due to point cloud's irregular format, analyzing this data using deep learning algorithms is quite challenging. In this ...
XXX Pα SE: SE for Squeeze-and-Excitation with reduction-ratio = 8; XXX GWCβ PWC: GWCβ PWC is the groupwise convolution with group size β followed by pointwise convolution; XXX DWC PWC: DWC PWC is epthwise convolution followed by pointwise convolution; XXX PC: PC is part value P =...
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs Pravendra Singh Vinay Kumar Verma Piyush Rai Vinay P. Namboodiri Department of Computer Science and Engineering, IIT Kanpur, India {psingh, vkverma, piyush, vinaypn}@cse.iitk.ac.in Abstract We present a...
在grouped heterogeneous kernel-based convolution 中GHconv被提出,本文从对比普通卷积和异质核卷积的角度进行对比和学习. To overcome the disadvantage of a large number of traditional convolution parameters, GHConv is proposed.GHconv作为轻量级模块,可以进行即插即用的应用. 让我们考虑对参数量进行一个对比计算:...
论文翻译:HetConv-Heterogeneous Kernel-Based Convolutions for Deep CNNs Abstract 我们提出了一种新颖的深度学习架构,其中卷积操作利用了异构内核。与标准卷积运算相比,所提出的HetConv(基于异构内核的卷积)减少了计算(FLOPs)和参数的数量,同时仍保持表示效率。为了证明我们提出的卷积的有效性,我们在标准卷积神经网络(...
Kernel-based convolution method to calculate sparse aerial image intensity for lithography simulation [J]. 半导体学报, 2003, 24(4): 357-361.SHI Z, WANG G X, YAN X L, et al.A kernel-based convolution method to calculate sparse aerial image for lithography simulation[J]. Chinese J of ...
During convolution operation, the padding remains the same so that the size of the feature maps remain the same as the input image. These feature maps are passed through the ReLU activation function to reduce the linearity. Further, max pooling is applied on the feature maps with a pool size...
In this paper, we proposed a convolution kernel initialization method based on the two-dimensional principal component analysis (2DPCA), in which a parametric equalization normalization method is used to adjust the scale between each neuron weight. After that the weight initial value can be ...
Chaubey, V., Nair, M.S., Pillai, G.: Gene expression prediction using a deep 1D convolution neural network. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1383–1389. IEEE (2019) Google Scholar Xie, R., et al.: A predictive model of gene expression using a...