CNN소개 Keynote입니다. 관련 영상을 촬영하다 무산되어 자료만 올립니다.

1. Introduction to CNN

3. (Arti cial Neural Networks)
Input Output

4. ?
2 , 154 .
77 :
77 :
, , .
.

5. DNN : Fully Connected

6. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
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200 by 200 ??
40000
~ 20
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7. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
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200 by 200
40000
10 * 10
4,000,000
, - (sparse) Connection

8. https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
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200 by 200
100
10 * 10
10,000

9. NN CNN ?

10. NN

11. CNN .

12. Feature Extraction
Machine Learning
End-to-End Feature Extraction
Deep Learning
.

13. WhyCNN?
그래서 왜 CNN을 사용하고, CNN의 특징은 무엇일까

14. 1. Sparse Connection
2. Parameter Sharing
3. Translational Invariant
CNN Main Ideas

15. I. Sparse Connection
Sparse Connection
( )

16. I. Sparse Connection
layer receptive eld
3x3 = 5x5

17. II. Parameter Sharing
(= ) parameter sharing

18. III. Translational Invariant

19. ( CONV layer Pooling Layer) andCNN

20. Activation map , Depth CNN

22. 32x32 5x5 28x28 activation map

23. 6 6 activation map

24. ,
activation map

25. 1. CONV(Convolution)
2. POOL(Pooling)
3. FC(Full Connection)
CNN Layer

26. Conv Layer

27. ( )

28. .

29. Red Green Blue
Channel ?
RGB, 3

30. Conv Layer
Feature map ?
?

31. Stride
kernel size
.
1, 2 1/4

32. Padding
stride output
side e ect
Zero Padding(0 )
+

33. Pooling Layer

34. Pooling Stride

35. Pretrained Model