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.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||ImageNet||Xception||Top 1 Accuracy||79%||# 10|
|Image Classification||ImageNet||Xception||Top 5 Accuracy||94.5%||# 10|