Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Paper summary This paper presents a combination of the inception architecture with residual networks. This is done by adding a shortcut connection to each inception module. This can alternatively be seen as a resnet where the 2 conv layers are replaced by a (slightly modified) inception module. The paper (claims to) provide results against the hypothesis that adding residual connections improves training, rather increasing the model size is what makes the difference.
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, Christian and Ioffe, Sergey and Vanhoucke, Vincent
arXiv e-Print archive - 2016 via Bibsonomy
Keywords: dblp


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