- 前言: ResNet
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目录
0 More Details: HomeLink
1 Why ResNet
The deeper the network,
The more parameters,
The worse results we’ll get
WERIED!!!
WHY???
Too deep, accuracy saturated
Gradient vanished
How???
1.1 Structure: 2Qs
Structure
We can use conv/pool to reduce the size,
What about the shortcut?
How can we add 2 parts without same resolution?
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Get rich set of primary features
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Channel of input layer is less, big kernel doesn’t have to mean great of params
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Computing reason:
Res:7 * 7 * 3 * 64 *112 * 112 = 120M
VGG: 3 * 3 * 3 * 64 * 224 * 224 +
3 * 3 * 64 * 64 * 224 * 224 +
3 * 3 * 64 * 128 * 112 *112 +
3 * 3 * 128 * 128 * 56 * 56
= x >>120M * 2
1.2 VggNet & Small kernel
Small kernel always better?
NO
Sacrificing more real details of the image
1.3 Structure: Revisit
50-layer : 49 Conv layer
conv1: 7 * 7
before 50-layer no 1 * 1
50-layer, num of channel: less less more…
how add between left and right ? add conv in left…
2 Structure: Advanced?
deeper
wider
3 Why better?
1 Solved gradient vanishing by using shortcut
2 Can be seen as assembled models