πŸ“• Node [[depthwise_separable_convolutional_neural_network_(sepcnn)]]
πŸ“„ Depthwise_Separable_Convolutional_Neural_Network_(Sepcnn).md by @KGBicheno

depthwise separable convolutional neural network (sepCNN)

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A convolutional neural network architecture based on Inception, but where Inception modules are replaced with depthwise separable convolutions. Also known as Xception.

A depthwise separable convolution (also abbreviated as separable convolution) factors a standard 3-D convolution into two separate convolution operations that are more computationally efficient: first, a depthwise convolution, with a depth of 1 (n βœ• n βœ• 1), and then second, a pointwise convolution, with length and width of 1 (1 βœ• 1 βœ• n).

To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions.

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