Search results with tag "Depthwise separable convolutions"
Xception: Deep Learning With Depthwise Separable …
openaccess.thecvf.comules and depthwise separable convolutions are also possible: in effect, there is a discrete spectrum between regular convo-lutions and depthwise separable convolutions, parametrized by the number of independent channel-space segments used for performing spatial convolutions. A regular convolution (preceded by a 1x1 convolution), at one extreme ...
fchollet@google - arXiv
arxiv.orgDepthwise separable convolutions, which our proposed architecture is entirely based upon. While the use of spa-tially separable convolutions in neural networks has a long history, going back to at least 2012 [12] (but likely even earlier), the depthwise version is more recent. Lau-rent Sifre developed depthwise separable convolutions
MobileNetV2: Inverted Residuals and Linear Bottlenecks
openaccess.thecvf.comDepthwise separable convolutions are a drop-in re-placement for standard convolutional layers. Empiri-cally they work almost as well as regular convolutions but only cost: hi ·wi ·di(k 2 +d j) (1) which is the sum of the depthwise and 1 × 1 pointwise convolutions. Effectively depthwise separable convolu-
fzhangxiangyu,zxy,linmengxiao,sunjiang@megvii.com arXiv ...
arxiv.orgdepthwise separable convolutions or group convolutions into the building blocks to strike an excellent trade-off between representation capability and computational cost. However, we notice that both designs do not fully take the 1 convolutions (also called pointwise convolutions in [12]) into account, which require considerable complex-ity.
Neural Architecture Search: A Survey
www.jmlr.orgDeep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect ... operations like depthwise separable convolutions (Chollet, 2016) or dilated convolutions (Yu