本文探讨了结合残差连接的Inception架构(Inception-ResNet)在深度学习中的应用，介绍了如何将残差连接引入到Inception模块中以解决深层网络训练问题，并讨论了批量归一化在该架构中的作用。
1. Motivation: residual connections work well for deep network ==> can be combined with Inception (Inception-ResNet)
a. replace filter concatenation of inception with residual
connection
(the 1 x 1 conv after inception layer aims to scale up
the dimension before adding to the input)
b. scaling down the residuals (multiple scaling factor 0.1~0.3) before addition ==> stabilize the training (prevent weights from going to 0)
2. Batch normalization: on top of traditional layers (excluding summation layer to reduce computational cost), prevent saturating.
3. performance.(for detailed differences bw models, please refer to the paper)
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