Deep...

PowerPoint

Deep Learning Deep Learning #general / YouTube

2

Deep LearningDeep Learning2012: GoogleGmailwebDeep Learning

Deep LearningYouTubeNHK

3

Deep Learning Deep LearningDeep Learning

Deep LearningDeep Learning

Deep LearningDeep Learning4

(CNN)Google

5

1.

6

7

-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*=

16

0-100200-10000-12-1000

111111111***

17

2. Convolutional Neural Network18

19

(0, 0)(0, 1)(27, 26)(27,27) 01(28x28 gray-scale)

8928x28=784

20

(0, 0)R(0, 0)G(479, 359)G(479, 359)B (360x480 RGB)

360x480x3= 518,400

#1 100 = 51,840,000

21

2200000000001010000105100000010501000105001000101010101000000000-1-12-12-12-1-10-10-253510-10-25500-15-255010-20-5150-20102010-15-5200*=

23

3x3=9 1

24

3x3=9 1

25

3x3=9 1

26

3x3=9 1

27

3x3=9 1

28

3x3=9 1

29

3x3=9 1

30

3x3=9 1

31

3x3=9 1

32

3x3=9 1

33

3x3=9 2

34

3x3=9 3

35

3x3=9 3

36

3x3=9 3

37

3x3=9 3R

38

RG

3x3x2 = 18 3

39

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

42

Google(1)https://youtu.be/jajksuQW4mc

43

Google(2)https://youtu.be/DDRa5ASNdq4

44

Google(3)https://youtu.be/sRIfSAAQd9w

45

Google(4)https://youtu.be/EI5CbTUIjCM

46

Google(5)https://youtu.be/Fif8uipYuHE

47

Google(6)https://youtu.be/qVP574skyuM

48

Google(7)https://youtu.be/VxhSouuSZDY

49

Google(8)https://youtu.be/T5jPt5gAxMQ

50

51

Depth

4.

52

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

54

AlexNet2012ILSVRC - - : 60M55

GoogLeNet2014ILSVRCGoogle UP: 5M (AlexNet1/10)Google56

VGGnet2014ILSVRCGoogLeNet3x32x2C-P-C-P-CC-P-CC-P-CCC-P-CCC-P-138MAlexNetGoogLeNet57

5.

58

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

60

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

63

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

64

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

66

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

68

4. Dong, et.al. Learning a Deep Convlutional Network for Image Super-Resolution, 2014https://github.com/nagadomi/waifu2x

69

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

1/16

1/8

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





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



CNN


9. Gatys, et.al. A Neural Algorithm of Artistic Style, 201593

94

CNN

http://arxiv.org/pdf/1412.6806v3.pdf (Global Average Pooling)

https://github.com/kjw0612/awesome-deep-vision

95



Deep LearningDeep Learning96

Deep Learning

https://goo.gl/11PTVKGoogle97

5/16 Deep Learning(1)5/23 Deep Learning(2)5/30 Deep Learning(3) Deep Learning/

Slack #ai 98

Deep...

Technology

Transcript of Deep...