![]() ![]() Rather to a more efficient use of model parameters. ![]() Inception V3, the performance gains are not due to increased capacity but Since the Xception architecture has the same number of parameters as Larger image classification dataset comprising 350 million images and 17,000Ĭlasses. Inception V3 was designed for), and significantly outperforms Inception V3 on a Xception, slightly outperforms Inception V3 on the ImageNet dataset (which A year of developing Keras, using Keras, and getting feedback from thousands of users has taught us a lot. It has made tremendous progress since, both on the development front, and as a community. ![]() This observation leads us to propose a novel deep convolutional neural networkĪrchitecture inspired by Inception, where Inception modules have been replaced Keras was initially released a year ago, late March 2015. In this light, a depthwise separable convolution canīe understood as an Inception module with a maximally large number of towers. Networks as being an intermediate step in-between regular convolution and theĭepthwise separable convolution operation (a depthwise convolution followed byĪ pointwise convolution). Download a PDF of the paper titled Xception: Deep Learning with Depthwise Separable Convolutions, by Fran\cois Chollet Download PDF Abstract: We present an interpretation of Inception modules in convolutional neural ![]()
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