Example: tourism industry
Learning With Depthwise Separable
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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,[email protected] 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.