Xception: Deep Learning with Depthwise Separable Convolutions

François Chollet

   Papers with code   Abstract  PDF

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers... (read more)

Benchmarked Models

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REPO
CODE RESULT
PAPER RESULT
ε-REPRODUCED
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Xception
79.0%
79.0%