姚伟峰
做研究就像比武论剑一样,要论剑就要到华山论剑,如果你一定要去太行山论剑,去挺进大别山,那别人只能当你是游击队,永远也别想成正规军。在计算机视觉领域,农村是永远也包围不了城市的。华山以外,很难论出好剑。
- 汤晓鸥
AlexNet
Year
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2012
Achievement
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ILSVRC-2012 winner, achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry in ILSVRC-2012 competition.
Current Affiliation
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Toronto University Google
(right: Hinton, mid: Alex, left: Ilya Sutskever)
Features
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Bring deep learning back to CV community & industry.
Topology
GoogLeNet-v1
Year
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2015 CVPR
Achievement
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ILSVRC-2014 winner with top-5 test error rate of 7.9%
Current Affiliation
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Google (Christian Szegedy)
Features
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More Accurate(Representative)
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Wider
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Introduce Inception-v1 (Deep Dream) with heterogeneous combination of convolutions
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Deeper
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22 layers while AlexNet is 8
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Faster
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Special designed Inception to decrease computation
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Less parameters 4M while AlexNet is 61M (only 1 FC layer)
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VGG
Year
-
2015 ICLR
Achievement
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ILSVRC-2014 runner-up with top-5 test error rate of 7.3%
Current Affiliation
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Oxford University Google (Karen Simonyan)
Features
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More Accurate(Representative)
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Wider
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feature map number up to 512
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Deeper
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16(VGG-16) and 19(VGG-19)
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-
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Faster
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Simple factorization: use multiple 3x3 kernel to simulate bigger kernel. (2 to simulate 5x5, 3 to simulate 7x7)
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No LRN is involved
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While, VGG greatly increased the parameter number, from 61M(AlexNet) to 138M(VGG-16) and 144M(VGG-19).
Inception-v2 & Inception-v3
Year
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2015 Dec
Achievement
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top-5 test error rate of 5.6% (v3)
Current Affiliation
-
Google (Christian Szegedy)
Features
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More Accurate(Representative)
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Wider
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New inception modules
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Deeper
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v3 depth 17 if treating Inception as one, 47 layers in fact.
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More Accurate through tricks
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Batch Normalization - v2, v3
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location
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algorithm
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Label Smooth - v3
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BN auxiliary classifier - v3
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Faster
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Factorization: - v3
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Grid Size Reduction - v3
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Batch Normalization - v2, v3
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Arch
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Inception-v2
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v1 with BN layers
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Inception-v3
ResNet
Year
-
2015 Dec
Achievement
-
ILSVRC-2015 winner with top-5 test error rate of 5.7%
Current Affiliation
-
Microsoft Facebook (He Kaiming)
Features
Try to fix the bad behavior of CNN in linear component representation.
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Shortcut
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CNN to approximate non-linear part while shortcut to simulate linear part
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More Accurate(representative)
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Wider
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feature map number up to 3072
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Deeper
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up to 152 layer
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-
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Faster
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Small kernel: all 3x3 except first layer(7x7)
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Only one FC layer with 100M parameters in 152-layer arch
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Arch
Inception-v4
Year
-
2016
Achievement
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top-5 test error rate of 4.2%
Current Affiliation
-
Google (Christian Szegedy)
Arch