Transcription of Xception: Deep Learning With Depthwise Separable …
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Xception: Deep Learning With Depthwise Separable …
Xception: Deep Learning with Depthwise Separable ConvolutionsFranc ois CholletGoogle, present an interpretation of Inception modules in con-volutional neural networks as being an intermediate stepin-between regular convolution and the Depthwise separableconvolution operation (a Depthwise convolution followed bya pointwise convolution). In this light, a Depthwise separableconvolution can be understood as an Inception module witha maximally large number of towers. This observation leadsus to propose a novel deep convolutional neural networkarchitecture inspired by Inception, where Inception moduleshave been replaced with Depthwise Separable show that this architecture, dubbed Xception, slightlyoutperforms Inception V3 on the ImageNet dataset (whichInception V3 was designed for), and significantly outper-forms Inception V
depthwise separable convolutions in the TensorFlow framework [1]. • Residual connections, introduced by He et al. in [4], which our proposed architecture uses extensively. 3. The Xception architecture We propose a convolutional neural network architecture based entirely on depthwise separable convolution layers.
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