Depth-wise convolution and Depth-wise Separable Convolution可分离卷积,程序员大本营,技术文章内容聚合第一站。
Depth-wise separable convolution :深度分离卷积,出自mobilenet和xception。 MobileNet是基于深度可分离卷积的,通俗来说,深度可分离卷积就是:把标准卷积分解成深度卷积(depthwise convolution)和逐点卷积(pointwise convolution)。这么做的好处就是可以再损失精度不多的情况下大幅度降低参数量和计算量。分解过... ...
首先看看depth-wise卷积到底是什么: https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728 这篇文章讲的非常全面,应有尽有,我不做重复工作了,捡重要的来说: 首先,对于普通卷积: 我们...
【深度可分离卷积性能研究】《Depth-wise Separable Convolutions: Performance Investigations》by Timothy Liu http://t.cn/A6PGZAxQ GitHub:http://t.cn/A6PGZAxH
The proposed backbone uses point-wise separable (PWS) and depth-wise separable convolutions, which are more efficient than standard convolution. The PWS convolution utilizes a residual shortcut link to reduce computation time. We also propose a SFPN that comprises concatenation, transformer encoder-...
times 3 kernels instead of the traditional 5×55\times 5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to utilize...
First, we use 3×3 kernels instead of the traditional 5 × 5 kernels and optimize convolution kernels in the preprocessing layer. The smaller convolution kernels are used to reduce the number of parameters and model the features in a small local region. Next, we use separable convolutions to...
MoblieNet is a stack of the separable convolution modules which are composed of depth-wise卷积和1x1卷积(point-wise 卷积) 可分离卷积(separable conv)就是在spatial和channel独立地进行卷积。计算量从 K*K*N*H*W*M变为K*K*H*W*N + N*H*W*M,即HWN(K*K+M),M是远大于K*K的(比如K=3,M=32...
Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Recently, depth-wise separable convolution has been proposed for image recognit...
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions 1.论文摘要 提出了一种time-depth separable 的卷积网络结构,作为ED模型的encoder,在显著减少了参数量的同时增加了计算速度,并且可以维持较大的感受野范围,在noisy LibriSpeech test set 取得了WER 22%的提升。 2.模型结构 encoder TDS ...