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PowerPoint
Deep Learning Deep Learning #general / YouTube
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Deep LearningDeep Learning2012: GoogleGmailwebDeep Learning
Deep LearningYouTubeNHK
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Deep Learning Deep LearningDeep Learning
Deep LearningDeep Learning
Deep LearningDeep Learning4
(CNN)Google
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1.
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-1-12-12-12-1-1*
8-1-12-12-12-1-1*00000000001010000105100000010501000105001000101010101000000000
900000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
000000000
=00
1000000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
0000000010
0-10
=-10
1100000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
00000100105
0-10-25
=-25
151200000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
0000101010510
0-10-25
=15
1300000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
000101005100
0-10-2510
=1015
1400000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-1
0000000010
0-10-2510-10
=-1015
1500000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-10-10-251510-10-25500-15-255010-20-5150-20102010-15-5200*=
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0-100200-10000-12-1000
111111111***
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2. Convolutional Neural Network18
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(0, 0)(0, 1)(27, 26)(27,27) 01(28x28 gray-scale)
8928x28=784
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(0, 0)R(0, 0)G(479, 359)G(479, 359)B (360x480 RGB)
360x480x3= 518,400
#1 100 = 51,840,000
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2200000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-10-10-253510-10-25500-15-255010-20-5150-20102010-15-5200*=
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 1
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3x3=9 2
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3x3=9 3
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3x3=9 3
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3x3=9 3
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3x3=9 3R
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RG
3x3x2 = 18 3
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3x3x3 = 27 3
RGB
(Convolution Layer)40(Fully-Connected Layer)
x
3. (Google)41
Googlehttps://www.udacity.com/course/deep-learning--ud730 https://goo.gl/UpnMOU
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Google(1)https://youtu.be/jajksuQW4mc
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Google(2)https://youtu.be/DDRa5ASNdq4
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Google(3)https://youtu.be/sRIfSAAQd9w
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Google(4)https://youtu.be/EI5CbTUIjCM
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Google(5)https://youtu.be/Fif8uipYuHE
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Google(6)https://youtu.be/qVP574skyuM
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Google(7)https://youtu.be/VxhSouuSZDY
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Google(8)https://youtu.be/T5jPt5gAxMQ
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Depth
4.
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ImageNet22,000140,000StanfordAmazon Mechanical Turkhttp://image-net.org/
ILSVRC (ImageNet Large Scale Visual Recognition Challenge)2012ILSVRCDeep Learning53
CNNAlexNetKrizhevsky, et.al. ImageNet Classification with Deep Convolutional Neural Networks, 2012.
GoogLeNetGoogleSzegedy, et.al. Going Deeper with Convolutions, 2014
VGGnetSimonyan, et.al. Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014
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AlexNet2012ILSVRC - - : 60M55
GoogLeNet2014ILSVRCGoogle UP: 5M (AlexNet1/10)Google56
VGGnet2014ILSVRCGoogLeNet3x32x2C-P-C-P-CC-P-CC-P-CCC-P-CCC-P-138MAlexNetGoogLeNet57
5.
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5-1.
1. Simonyan, et.al. Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014
ConvNet (VGGnet)224 x 224 RGB 3x3, 1, SameMAX 2x2, 24096 - 4096 - 1000()RGBReLU
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1. Simonyan, et.al. Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014
61DeepDNNModel 143
1.3M 50K
Model 1Model 2Model 3Model 4 224 x 224 x 3Conv3 - 64Conv3 - 64Conv3 - 64Conv3 - 64Conv3 - 64Conv3 - 64Conv3 - 64Max PoolingConv3 - 128Conv3 - 128Conv3 - 128Conv3 - 128Conv3 - 128Conv3 - 128Conv3 - 128Max PoolingConv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Conv3 - 256Max PoolingConv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Max PoolingConv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Conv3 - 512Max PoolingFully-Connected 4096Fully-Connected 4096Fully-Connected 1000
Top-5error10.4%9.9%8.8%8.1%
5-2.
2. Girshick, et.al. Rich feature hierarchies for accurate object detection and semantic segmentation, 2013 CNN SVM
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2. Girshick, et.al. Rich feature hierarchies for accurate object detection and semantic segmentation, 2013Uijlings, et.al. Selective Search for Object Recognition, 2013http://goo.gl/KwcUC9
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5-3.
3. Hong, et.al. Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, 2015CNNSVMCNNSVMSVMCNN(Saliency Map)Saliency MapBayesian https://youtu.be/PCd6xWTxdK4
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5-4.
4. Dong, et.al. Learning a Deep Convlutional Network for Image Super-Resolution, 2014End-To-End CNNBicubic3Conv(9x9) / 64Feat / 1Stride Conv(1x1) / 32Feat / 1Stride Conv(5x5) / 1Stride32x3291Sparse CodingCNNState-of-the-art
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4. Dong, et.al. Learning a Deep Convlutional Network for Image Super-Resolution, 2014https://github.com/nagadomi/waifu2x
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5-5.
5. Xie, et.al. Holistically-Nested Edge Detection, 201471
VGGnetCNNState-of-the-art
5-6.
6. Long, et.al. Fully Convolutional Networks for Semantic Segmentation, 201573
CNNCNN
(1/32)
6. Long, et.al. Fully Convolutional Networks for Semantic Segmentation, 201574
CNN1/32
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6. Long, et.al. Fully Convolutional Networks for Semantic Segmentation, 2015VGGnet
State-of-the-art75
5-7.
7. Liu, et.al. Predicting Eye Fixations using Convolutional Neural Networks, 201577CNNState-of-the-art
5-8.
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2016Generative Adverserial Networks (GAN)
Gn z x (generator)D x (discriminator)
D10GD
GDG79
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2016G DeconvolutionDCNN180
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2016GAN
[]
m nz m G m log(D()) + log(1-D())D
nz m G m log(1-D()) G81
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 201682
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 201683
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 201684
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 201685
8. Radfort, et.al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 201686
5-9.
9. Gatys, et.al. A Neural Algorithm of Artistic Style, 201588
641282569. Gatys, et.al. A Neural Algorithm of Artistic Style, 2015893 x 256 x 25664 x 256 x 256128 x 128 x 128256 x 64 x 64512 x 32 x 32256
649. Gatys, et.al. A Neural Algorithm of Artistic Style, 2015903 x 256 x 25664 x 256 x 256128 x 128 x 128256 x 64 x 64512 x 32 x 3264
9. Gatys, et.al. A Neural Algorithm of Artistic Style, 2015913 x 256 x 25664 x 256 x 256128 x 128 x 128256 x 64 x 64512 x 32 x 32
CNN
9. Gatys, et.al. A Neural Algorithm of Artistic Style, 2015923 x 256 x 25664 x 256 x 256128 x 128 x 128256 x 64 x 64512 x 32 x 32
9. Gatys, et.al. A Neural Algorithm of Artistic Style, 201593
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CNN
http://arxiv.org/pdf/1412.6806v3.pdf (Global Average Pooling)
https://github.com/kjw0612/awesome-deep-vision
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Deep LearningDeep Learning
Deep LearningDeep Learning
Deep LearningDeep Learning96
Deep Learning
https://goo.gl/11PTVKGoogle97
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