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.
This observation leads us to propose a novel deep convolutional neural network
architecture inspired by Inception, where Inception modules have been replaced
with depthwise separable convolutions. We show that this architecture, dubbed
Xception, slightly outperforms Inception V3 on the ImageNet dataset (which
Inception V3 was designed for), and significantly outperforms Inception V3 on a
larger image classification dataset comprising 350 million images and 17,000
classes. Since the Xception architecture has the same number of parameters as
Inception V3, the performance gains are not due to increased capacity but
rather to a more efficient use of model parameters.
[1610.02357] Xception: Deep Learning with Depthwise Separable Convolutions