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Introduction to CNN
Introduction to CNN
(Arti cial Neural Networks)
Input Output
?
2 , 154 .
77 :
77 :
, , .
.
DNN : Fully Connected
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
?
200 by 200 ??
40000
~ 20
- ??
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
?
200 by 200
40000
10 * 10
4,000,000
, - (sparse) Connection
https://www.slideshare.net/milkers/lecture-06-marco-aurelio-ranzato-deep-learning
, ?
200 by 200
100
10 * 10
10,000
NN CNN ?
NN
CNN .
Feature Extraction
Machine Learning
End-to-End Feature Extraction
Deep Learning
.
WhyCNN?
그래서 왜 CNN을 사용하고, CNN의 특징은 무엇일까
1. Sparse Connection
2. Parameter Sharing
3. Translational Invariant
CNN Main Ideas
I. Sparse Connection
Sparse Connection
( )
I. Sparse Connection
layer receptive eld
3x3 = 5x5
II. Parameter Sharing
(= ) parameter sharing
III. Translational Invariant
( CONV layer Pooling Layer) andCNN
Activation map , Depth CNN
Introduction to CNN
32x32 5x5 28x28 activation map
6 6 activation map
,
activation map
1. CONV(Convolution)
2. POOL(Pooling)
3. FC(Full Connection)
CNN Layer
Conv Layer
( )
.
Red Green Blue
Channel ?
RGB, 3
Conv Layer
Feature map ?
?
Stride
kernel size
.
1, 2 1/4
Padding
stride output
side e ect
Zero Padding(0 )
+
Pooling Layer
Pooling Stride
Pretrained Model

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