Convolutions

Depthwise Separable Convolution

Introduced by Chollet in Xception: Deep Learning With Depthwise Separable Convolutions

While standard convolution performs the channelwise and spatial-wise computation in one step, Depthwise Separable Convolution splits the computation into two steps: depthwise convolution applies a single convolutional filter per each input channel and pointwise convolution is used to create a linear combination of the output of the depthwise convolution. The comparison of standard convolution and depthwise separable convolution is shown to the right.

Credit: Depthwise Convolution Is All You Need for Learning Multiple Visual Domains

Source: Xception: Deep Learning With Depthwise Separable Convolutions

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Image Classification 78 11.56%
Object Detection 51 7.56%
Classification 40 5.93%
Quantization 34 5.04%
Semantic Segmentation 30 4.44%
Instance Segmentation 11 1.63%
Management 9 1.33%
Ensemble Learning 8 1.19%
Computational Efficiency 8 1.19%

Categories