论文笔记:Inception-V4, Inception-ResNet
2016-06-17 12:36
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1. Motivation: residual connections work well for deep network ==> can be combined with Inception (Inception-ResNet)
2. architecture
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)
2. architecture
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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