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...
1、卷积加速技术 作者对已有的新型卷积划分如下:标准卷积、Depthwise 卷积、Pointwise 卷积、群卷积(相关介绍见『高性能模型』深度可分离卷积和MobileNet_v1),后三种卷积可以取代标准卷积,使用方式一般是 Depthwise + Pointwise 或者是 Group + Pointwise 这样的两层取代(已有网络架构中的)标准卷积的一层,成功的在不损...
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:...
To achieve this, we propose a new type of convolution - heterogeneous convolution. The convolution operation can be divided into two cate- gories based on the types of the kernel: • Homogeneous convolution using a traditional convolu- tional filter (for example st...
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 ...
3.convolution- the action of coiling or twisting or winding together change of shape- an action that changes the shape of something Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc. convolution ...
图 1，使用 CK 的 Tensor Coordinate Transformation 基础模块将 convolution 算子表达成 GEMM 算子 图 2，CK 的组成（下：可复用的基础模块；上：独立算子与融合算子）代码结构 CK 库结构分为四层，从下到上分别是：Templated Tile Operator，Templated Kernel and Invoker，Instantiated Kernel and Invoker 和 ...
PointConvFormer: Revenge of the Point-based Convolution May 22, 2023 | research area Computer Vision, research area Methods and Algorithms | conference CVPR We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConv...
Firstly, we enhance the VMamba model with two additional convolution layers. This extension aims to improve pixel-level classification accuracy, resulting in a hybrid CNN-Mamba context branch with strong semantic recognition. Secondly, to mitigate potential spatial information loss in the context branch...