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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 β n β 1), and then second, a pointwise convolution, with length and width of 1 (1 β 1 β n).
To learn more, see Xception: Deep Learning with Depthwise Separable Convolutions.
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