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Status: Unchecked ×Keyword: Xception ×Clear all

3 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Xception: Deep Learning with Depthwise Separable Convolutions

   François · arXiv (Cornell University) · 2016

   The paper reads Inception modules as a step towards depthwise separable convolutions and proposes Xception, an architecture built entirely from them, which it reports outperforms Inception V3.

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   1. UncheckedThe Xception network slightly beats Inception V3 on ImageNet and does so by a larger margin on a 350-million-image, 17,000-class dataset.“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.”
   2. UncheckedA depthwise separable convolution can be seen as an Inception module with the largest possible number of parallel branches, called towers.“In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers.”

2. Computer Science › Advanced Neural Network Applications

   Channel Pruning for Accelerating Very Deep Neural Networks

   He, Zhang and Sun · arXiv (Cornell University) · 2017

   The paper introduces a channel pruning method that speeds up trained deep convolutional networks, and reports results on VGG-16, ResNet and Xception with small losses in accuracy.

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   1. UncheckedThe authors report that their pruned VGG-16 network runs five times faster with only a 0.3% increase in error, which they describe as state-of-the-art.“Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error.”