際際滷

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[GomGuard] ???? YOLO ?? - ??? ??? ?? ???

J
JungHyun Hong

??, perceptron, cnn, r-cnn, fast r-cnn, faster r-cnn ? backpropagation, activation function, batch normalization, cost function, optimizer ? ???? ??? ????? ?? ??? ??? ????. mail : knholic@gmail.com blog : gomguard.tistory.com

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??? ?? ?? YOLO ??
??? knholic@gmail.com gomguard.tistory.com
??? ??? ????
??? ?? ??? ?? ??? ?? ??? ?? ?? ???? ??? ??? ????? ???
? ? ?? ??? ?? [??? ?? ??? ??] ?? ???
??? ??? ??? : ?? ???? ???? ??? ??? ???? ??? ??? ?? ????? ?? ???? ???.
??? ?? ?? ?? ?? : Perceptron ? = 1 ( ?1 ?1+ ?2 ?2 + ? − 0 ) 0 ( ?1 ?1+ ?2 ?2 + ? < 0 ){ x1 ?1 ?2 ?1 ?2 ? Bias Input Input Activation Function ? Weight
Perceptron ? ???? ?? ??: ?? ?? ???? ???????? (?) = 1 (? − 0 ) 0 (? < 0 ){ ???? ??? ?? 0? ?? ?? ??
?? ??? AND ??? ?? ?1 ?2 ? AND Gate ? ???? ?1 ?2 ? 0 0 0 0 1 0 1 0 0 1 1 1 AND Gate ? ???
AND ???? ?? ?? ?? ?? 0 0 0 1 ??? ??? 0 ? 1? ??? ? ?? AND ???
?? XOR ?????? 0 0 1 1 ? ? ? ? ??? ???? 0 ? 1? ??? ? ?? XOR ???
XOR ??? ?1 ?2 ?AND AND OR XOR ???? AND ? OR ???? ???? ?? ? ???
?? ???? ?? ?? ???? : MLP Input Layer ?1 ?2 x11 Output Layer ? ?＞ Hidden Layer x11 ?2 ?1 ?1 ＞ ?2 ＞ ?00 (0) ?01 (0) ?10 (0) ?11 (0) ?20 (0) ?21 (0) ?00 (1) ?10 (1) ?20 (1)
MLP ? XOR ???? ???? Input Layer ?1 ?2 x1? Hidden Layer NAND Gate OR Gate Output Layer XOR Gate MLP ?? XOR ???? ??? ? ? ??
Layer ? ??? ?? ??? ? ?? ?? 1 hidden layer 2 hidden layer 3 hidden layer
?? ?? ? ?? ? ???? MLP ? ??? ???????
?? ??? ?? ?? ????
??? ? ?? ?? ??? ?? ?? ??? ?? ?? ???? ???? ?? ??? ???
?? ??? ? ????? ??? ?????? ???? ?? ?????? ??? ? ??
??? ?? ?? ??? ?? ???? ?? ?? ?? ??? ??? ???? ?? ?? ?????
?? ??? ??? ?? ?? ??? ??? ?? ??? ?? ??
1. ????? ??? 2. ?(????????) ? ??? 3. weight ? ???? ?? ? ??? = ? ? ???(?)
?? ???? ???? : MSE Minimum Squared Error = 1 ? σ? ??? ? ?? 2 ?0 ?1 ?2 ?3 ?4 ??0 ??1 ??2 ??3 ??4 # ? ??? ???? ??? ???? ???? ??
?? ???? ???? : ACE Averaged Cross Entropy Error = ? 1 ? σ? ?? ??? ??? ?0 ?1 ?2 ?3 ?4 log(??0) log(??1) log(??2) log(??3) log(??4) # ??? One-hot ??? ?? ?? ???? ????
?+ = ? ? ? ? ?? ?? ??? ????? ???? learning rate : ??? ??? ???? gradient : ?? ???? ????
??? ????? ?? ?? ? ?? 0´
??? 0?? 1?? ????? ?? ??? ??? ????
???????(?) = 1 1+??? ??? ?? Sigmoid ???? ???????? (?) = 1 (? − 0 ) 0 (? < 0 ){
?? ??? Sigmoid ? ???? ??? ??
MLP ? ????? ??? ??? ??? ?????
?10 (0) ?11 (0) ?1 0.5 0.15 0.2 0.075 0.1 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ???? = 1 2 ? ?????? ? ?????? 2 = 1 2 0.3 ? 0.609 2 + 1 2 0.9 ? 0.621 2 = 0.087 ??? ??? ??? ??? ??? ??? ?? ? ?? ???? 0.087 Sigmoid Function Sigmoid Function Sigmoid Function Sigmoid Function
????? ??10 (1) = ????? ??20 ??20 ??20 ??20 ??10 (1) ???? = 1 2 ?????? ?1 ? ?20 2 + ?????? ?2 ? ?21 2 ????? ??20 = ?????? ?1 ? ?20 ? ?1 + 0 ?20 = ???????(?20) ??20 ??20 = ??????? ?20 ? 1 ? ??????? ?20 ?20 = ?10 (1) ?10 + ?20 (1) ?20 ??20 ??10 (1) = ?10 + 0 ?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10 ?? ?10 (1) ? ????? ????? ??20 ??20 ??20 ??20 ??10 (1)
?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10 ????? ??10 (1) = ????? ??20 ??20 ??20 ??20 ??10 (1) = ? ?????? ?1 ? ?20 ? ??????? ?20 ? 1 ? ??????? ?20 ? ?10 = ? 0.3 ? 0.609 ? 0.609 ? 1 ? 0.609 ? 0.518 = 0.0381 ?10 1 + = ? ? ? ? ????? ??10 1 = 0.4 ? 0.5 ? 0.0381 = 0.380 ?10 1 ? ?? ??? ??? ??? 0.0381, ??? ?10 1 + ?? 0.380
? ?? ?? ? ?? ??? ?? ?????
?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ?10 (0) ? ?10 (1) ? ?? ? ?, ? ? ??? ? ??? ????
?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ??? ???? ?? ? ? ?1? ? ?2 ? ?? ???? ???
?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ????? ??10 (0) = ????? ??10 ??10 ??10 ??10 ??10 (0) ???? = ? ?1 + ? ?2
?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10?10 (0) ?1 0.5 0.15 0.075 ????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??1 ??10 = ??1 ??20 ??20 ??20 ??20 ??10 = ? ?????? ?1 ? ?20 ? ??????? ?20 ? 1 ? ??????? ?20 ? ?10 (1) = ? 0.3 ? 0.609 ? 0.609 ? 1 ? 0.609 ? 0.4 = 0.0294 # ?10 ? ?1 ? ??? ??? 0.0294
?11 (1) ?2?21 ?21 0.5 0.209 0.621 0.9 0.518 ?10 ?10?10 (0) ?1 0.5 0.15 0.075 ????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??2 ??10 = ??2 ??21 ??21 ??21 ??21 ??10 = ? ?????? ?2 ? ?21 ? ??????? ?21 ? 1 ? ??????? ?21 ? ?11 (1) = ? 0.9 ? 0.621 ? 0.621 ? 1 ? 0.621 ? 0.5 = ? 0.0328 # ?10 ? ?2 ? ??? ??? -0.0328
????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??2 ??10 = ?0.0345 ??1 ??10 = 0.0305 ??10 ??10 = ??????? ?10 ? 1 ? ??????? ?10 = 0.0294 ? 0.0328 ? 0.249 ? 0.5 = ?0.00042 ?10 0 + = ? ? ? ? ????? ??10 0 = 0.15 ? 0.5 ? (?0.00042) = 0.1502 ??? ?10 0 + ? ?? 0.1502 # ?10 (0) ? ???? ? ??? ??? -0.00042
?10 (0) ?11 (0) ?1 0.5 0.15 0.2 0.075 0.095 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.519 0.524 0.197 0.131 0.248 0.276 0.581 0.628 0.3 0.9 0.380 0.1502 0.250 0.527 0.478 0.1909 ???? = 0.087 ★ 0.076 ?? 1? ?? ?? 13% ??
? ?? ??? ??? Loss ?? ????
? ??? ??? ?? ? ?? ?? ? ?? ?? ? ? ????
? ??? ??? ????
??? ??? ? ????? ?? 1. ??? ??? ?? 2. ??? ??? ?? 3. ???? ????
???????(?) = 1 1+??? ?＞ (?) = 1 1+??? ( 1 ? 1 1+??? ) (0 , ?] C ??? ?, 0 ?? ?? Sigmoid ? ??? ??
?1 ?1 ?Input Sigmoid Function Sigmoid Function Sigmoid Function ?2 ?3 ?1 ?1 ?2 ?2 ?3 ?3 ?1 = ?1 ? ?1 ?2 = ?1 ? ?2 ?3 = ?2 ? ?3 ?1 = ???????(?1) ?2 = ???????(?2) ?3 = ???????(?3) ? = ?3 # Sigmoid ? ?? ???? ???? ?? 0.659 ? ???? ?? ??
??? ? ? ???
?? ??3 = ?? ??3 ??3 ??3 ??3 ??3 = 1 ? ???????＞ ?3 ? ?2 ?? ??2 = ?? ??3 ??3 ??2 ??2 ??2 ??2 ??2 = 1 ? ???????＞ ?3 ? ?3 ? ???????＞ ?2 ? ?1 ?? ??1 = ?? ??2 ??2 ??1 ??1 ??1 ??1 ??1 = 1 ? ???????＞ ?3 ? ?3 ? ???????＞ ?2 ? ?2 ? ???????＞ ?1 ? ?1 ? ?1 ? ?2 ?3 ?3 ?3 ?2 ?2 ?1 ?1 ?3 = ???????(?3) ?3 = ?2 ? ?3 ? = ?3 ?2 = ???????(?2) ?2 = ?1 ? ?2 ?1 = ???????(?1) ?1 = ? ? ?1
?＞ (?) : (0 , ?] C ??? ?, 0 ?? ?? S¨ ? ???? ? ?? ??? ?? ? ?? ? ? ??? ????? 3? ?? S¨ ? ?? ?? ?? ?? ? ?? 0 ? ?? ??? ????? ???
??? ?? : Tanh Function ????(?) = ?2?+ 1 ?2??1 ?＞ (?) =1 ? tanh2(x) (0 , 1] C ??? 1, 0 ?? ??
??? ?? : Tanh Function ?＞ (?) =1 ? tanh2(x) (0 , 1] C ??? 1, 0 ?? ?? ? ?? Tanh ? Sigmoid ? ???? ??? ???? ???? 1??? ??? ??? ? ???
??? ?? : ReLU Function ????(?) = max 0, ? Rectified Linear Unit ?＞(?) = 1 (? − 0 ) 0 (? < 0 ){
??? ?? : ReLU Function ?＞(?) = 1 (? − 0 ) 0 (? < 0 ){ ReLU ? ??? ??? ???? 0, 1 ?? ? ???? ??? ??? ??? ? ???? ?? ????? ?? ?????
???? PReLU, Leaky ReLU, SoftPlus ? ??? ?? ?? ???? ??? ??? ??? ????? ???
???? ???? ?? ?? ?? ?????
???? ??? ??? ?? ?? ???? ????? ??? ??? ??????
??? ???? ???? ????? ?? Xavier ? He initialization ? ?????
??? ??? ???? Activation Function Initialization Code Sigmoid Xavier np.random.randn(n_input, n_output) / sqrt(n_input) ReLU He np.random.randn(n_input, n_output) / sqrt(n_input / 2) 1. Gaussian ?? ??? ?? 2. ????? ????? ??? Xavier Initialization 3. ????? ??? ????? ??? He Initialization
??? ??? ???? Activation Function Initialization Code Sigmoid Xavier np.random.randn(n_input, n_output) / sqrt(n_input) ReLU He np.random.randn(n_input, n_output) / sqrt(n_input / 2) Sigmoid ??? ? Xavier , ReLU ??? ? He ? ???? ????
??? ???? ???? ????? ??? ??? ??? ???? ???? ???? ??? ????
? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ??? Input ?? ??? ???? ??? ???? Output ?? ??? ???? ???? ????
?? ??? ???? ??? ? ????? ?? ?? ?????? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
?? ???? ???? ???? ???? ?? ??? ??? ? ???? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
??? ???? ?? ??? ??? Output ?? ??? ? ? ????? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
??? ???? ?? ???? ?? ??? ??? ?? ???? ? ????? ??? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
Batch Normalization ? Training ?? ??? ???? ?? Vanishing Gradient ? ???? ???? ????? ? ? ????
?? ??? ? ??? ????? ? ??? ???
? ??? ??? ? ?????
10000 Data Gradient Descent Stochastic Gradient Descent 2500 Data 2500 Data 2500 Data 2500 Data Gradient Descent ??? ?? ???? ???? ??? ??? ?????
???? ??? ??? ?? ? ???? ?? ??? ?? ? ??? ??? ???? 10000 Data Gradient Descent Stochastic Gradient Descent 2500 Data 2500 Data 2500 Data 2500 Data
10000 Data Gradient Descent Stochastic Gradient Descent 2500 Data 2500 Data 2500 Data 2500 Data SGD GDStart End ? ???? ?? ? ??? ?? ?? ? ??? ?????
?? ???? ??? ??? ?? ????
?+ = ? ? ? ? ?? ?? ???? ?? ? ?? ??? Learning Rate ? Gradient ?? ? ???? ??? ??????
SGD ?? ?? Gradient ? Learning rate Momentum NAG Adam Adagrad RMSProp AdaDelta Nadam GD ?? : ??? - ???? ???? ???, ???? ??? ??????. ?? ???? ??? ? ??? ?? ??? ???? ??? ? ?? ??? ?? ?? ??? ???? ? ?????? ??? ?????? ?? ??? ? ??? ???? ??? ??? ??? ???? ??? ???? ???? ?????? ?? ???? ??? ??? ?? ?? ???? ???? ??? ?? ??? ??? gradient, learning rate ? ? ???? ??? ?? Adam ?? Momentum ?? NAG ? ???? Nesterov Accelerated Gradient
?? Optimizer ? ??? ?? ?? gradient learning rate
??? ? ?? ??? ?????
?? ?? ???!
? ???!
??? ?? ???!
?. ??????????
??? ????? ???? ? ?? ??? ?????
- 1 to 0 : 10?- 0 to 1 : 10? MLP ? ??? ? ??? ????? ???? ???? 20?
?? ????? ???? ???? ???? input ??? ??
??? ????? ??? ????
??? ? ?? ?? : ??, ??, ??, ??
CNN ? ??? ???? ??? ???????
1?? : ??, ????, ??, ????
2?? :?, ?, ?, ?
3?? :???!
64 X 64 Convolution Layer 32 X 32 Pooling Layer 32 X 32 Convolution Layer 16 X 16 Pooling Layer Fully-Connected layer 64 X 64 Input CNN ?? ??
CNN ? ?? ???? ? ??
?? ??? ??? CNN ??? ?? ??? 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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?, ? +2? + 1 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?+1 ? ?? Cost ? ?? ??? ???? ??? ??? ??????
?, ? +2? + 1 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?? ?+1 ? ??? ?? ????? ? ?????
?, ? +2? + 1 ? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ? ? Fully Connected Layer ? ?? ? ?? ? ???
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?＞ ?＞ ?+1 ? MaxPooling Layer ? ?? ? ? ???
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?00 ?+1 ? MaxPooling Layer ? ??? ?? ??? ??? ???? ??? ???
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?? ?+1? ? ?? ?+1? ??? ??? ??? ? ?? ???? ?? ????? ??? ??? ?? ????? ?????
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?? ?+1? ?? ?＞ ?＞ ? ? 3 ? 3 ???? ????? ? ??? ? ?? ????
?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?+1 ? ????? ? ?+1 ? Cost ? ??? ??? ???? ?? ?? ?+1 ??? ???
?, ? +2? + 1 ?? ?? ?+1 = ?? ?? ? ? ?? ? ? ?? ? ? ?? ? ? ? ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ? ? ? ?? ?＞ ?＞ ?+1 = ? σ ?＞ ?＞ ? ?＞ ?＞ ?+2 ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 = ? ?＞ ?＞ ?+2 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 = ?max[? ?+1] ?? ?? ?+1 = 1, ?? ? ?? ?+1 = max(?) { 0, ????????? ?? ?? ?+1 ?? ?? ?+1 = ? σ ? σ ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? ?? ?? ?+1 = ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? = ?????? ? ? ? ? ? ?1 ?? ?? ? ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?? ?+1 ? ??? ???? ??? ???? ??? ??? ???? ?? ???
2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ?, ? +2? + 1 ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?? ?+1 = ?? ?? ? ? ?? ? ? ?? ? ? ?? ? ? ? ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?＞ ?＞ ? = ? ?????? ? ? ? ? ? ? ?＞ ?＞ ?+2 ? ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? ? ?? ?+1, ? ?? ?+1 ?+ = ? ? ? ? ?? ?? = ? + ? ? ?????? ? ? ? ? ? ? ?＞ ?＞ ?+2 ? ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ?
?? ??? Filter ? ?????? ????´ ??? ???????
VGG, GoogleNet, ResNet ? ?? ???? ?? ??? ??? ??? ???? ?? ???? ???? ???
# Layers ?? ??? ?? ??? ??? ?? ??? ???? ????? ???
?? ? ???!
?? ??? ?? ??? ???
[GomGuard] ???? YOLO ?? - ??? ??? ?? ???
?? ?????
? ???? ?? ?? ??? ?? ?????
???? ?? ??? ???? CNN ?? ?????
Detect Model : OverFeat NewYork Univ - 2014 ??? ????? ?? ??? ???? ?? ???? ????
Detect Model : OverFeat NewYork Univ - 2014 ??? ????? ???? ??? ??? ?? ??? ?? ??
Detect Model : OverFeat NewYork Univ - 2014 ??? ??? ??? ???? ??
Detect Model : OverFeat NewYork Univ - 2014 ?? ??? ???? ????? ???
??? OverFeat? ??? ?? ?? ??? ?? ?? ????
?? ?? ??? ?? ?? CNN ?? ?????
Detect Model : R-CNN UC Berkeley - 2014 Input Region Proposals (Selective Search) classifier Compute Regions DOG CAT Classify # ??? ?? ?? ??? ??
OverFeat ??? ????? ??? ??? ??? ???? ??
R-CNN ?? ?? ??? ?? Region proposal, Classifier ? ??
Classifier ? ?? ?????
Detect Model : Fast R-CNN Microsoft - 2015 Region Proposals (Selective Search) Compute Regions classifier Classify DOG CAT convolution & pooling R-CNN ? ??? ?? ?? ?? ?? ?? ?? ??
Convolution, Pooling ?? ?? ???? ?? ?? Fast R-CNN
??? Region Proposal ??? ?????
Detect Model : Faster R-CNN Microsoft - 2015??? Region Proposal ??? ??? Region Proposal Network ?? ?? Region Proposal Network
R-CNN Fast R-CNN Faster R-CNN Time per image 50 secs 2 secs 0.2 secs SpeedUp 1x 25x 250x mAP 66.00% 66.90% 66.90% R-CNN benchmark Faster R-CNN ? ?? ?? 5 frame ? ??? ????? Real-Time Image Detection ? ???? ????
???? Yolo, SSD ? ??? ???? ??? ?? ?? ?? ????
Image Detection ? ??? ?? ??? ???? ????
?? ??? ?? ??? ? ??? ?? ??? ?? ? ????? ???
???? ???? ??? ?? ?? ??? ??? RNN, Gan ? ?????´ gomguard.tistory.com
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[GomGuard] ???? YOLO ?? - ??? ??? ?? ???

  • 4. ??? ?? ??? ?? ??? ?? ??? ?? ?? ???? ??? ??? ????? ???
  • 5. ? ? ?? ??? ?? [??? ?? ??? ??] ?? ???
  • 6. ??? ??? ??? : ?? ???? ???? ??? ??? ???? ??? ??? ?? ????? ?? ???? ???.
  • 7. ??? ?? ?? ?? ?? : Perceptron ? = 1 ( ?1 ?1+ ?2 ?2 + ? − 0 ) 0 ( ?1 ?1+ ?2 ?2 + ? < 0 ){ x1 ?1 ?2 ?1 ?2 ? Bias Input Input Activation Function ? Weight
  • 8. Perceptron ? ???? ?? ??: ?? ?? ???? ???????? (?) = 1 (? − 0 ) 0 (? < 0 ){ ???? ??? ?? 0? ?? ?? ??
  • 9. ?? ??? AND ??? ?? ?1 ?2 ? AND Gate ? ???? ?1 ?2 ? 0 0 0 0 1 0 1 0 0 1 1 1 AND Gate ? ???
  • 10. AND ???? ?? ?? ?? ?? 0 0 0 1 ??? ??? 0 ? 1? ??? ? ?? AND ???
  • 11. ?? XOR ?????? 0 0 1 1 ? ? ? ? ??? ???? 0 ? 1? ??? ? ?? XOR ???
  • 12. XOR ??? ?1 ?2 ?AND AND OR XOR ???? AND ? OR ???? ???? ?? ? ???
  • 13. ?? ???? ?? ?? ???? : MLP Input Layer ?1 ?2 x11 Output Layer ? ?＞ Hidden Layer x11 ?2 ?1 ?1 ＞ ?2 ＞ ?00 (0) ?01 (0) ?10 (0) ?11 (0) ?20 (0) ?21 (0) ?00 (1) ?10 (1) ?20 (1)
  • 14. MLP ? XOR ???? ???? Input Layer ?1 ?2 x1? Hidden Layer NAND Gate OR Gate Output Layer XOR Gate MLP ?? XOR ???? ??? ? ? ??
  • 15. Layer ? ??? ?? ??? ? ?? ?? 1 hidden layer 2 hidden layer 3 hidden layer
  • 16. ?? ?? ? ?? ? ???? MLP ? ??? ???????
  • 17. ?? ??? ?? ?? ????
  • 18. ??? ? ?? ?? ??? ?? ?? ??? ?? ?? ???? ???? ?? ??? ???
  • 19. ?? ??? ? ????? ??? ?????? ???? ?? ?????? ??? ? ??
  • 20. ??? ?? ?? ??? ?? ???? ?? ?? ?? ??? ??? ???? ?? ?? ?????
  • 21. ?? ??? ??? ?? ?? ??? ??? ?? ??? ?? ??
  • 22. 1. ????? ??? 2. ?(????????) ? ??? 3. weight ? ???? ?? ? ??? = ? ? ???(?)
  • 23. ?? ???? ???? : MSE Minimum Squared Error = 1 ? σ? ??? ? ?? 2 ?0 ?1 ?2 ?3 ?4 ??0 ??1 ??2 ??3 ??4 # ? ??? ???? ??? ???? ???? ??
  • 24. ?? ???? ???? : ACE Averaged Cross Entropy Error = ? 1 ? σ? ?? ??? ??? ?0 ?1 ?2 ?3 ?4 log(??0) log(??1) log(??2) log(??3) log(??4) # ??? One-hot ??? ?? ?? ???? ????
  • 25. ?+ = ? ? ? ? ?? ?? ??? ????? ???? learning rate : ??? ??? ???? gradient : ?? ???? ????
  • 27. ??? 0?? 1?? ????? ?? ??? ??? ????
  • 28. ???????(?) = 1 1+??? ??? ?? Sigmoid ???? ???????? (?) = 1 (? − 0 ) 0 (? < 0 ){
  • 29. ?? ??? Sigmoid ? ???? ??? ??
  • 30. MLP ? ????? ??? ??? ??? ?????
  • 31. ?10 (0) ?11 (0) ?1 0.5 0.15 0.2 0.075 0.1 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ???? = 1 2 ? ?????? ? ?????? 2 = 1 2 0.3 ? 0.609 2 + 1 2 0.9 ? 0.621 2 = 0.087 ??? ??? ??? ??? ??? ??? ?? ? ?? ???? 0.087 Sigmoid Function Sigmoid Function Sigmoid Function Sigmoid Function
  • 32. ????? ??10 (1) = ????? ??20 ??20 ??20 ??20 ??10 (1) ???? = 1 2 ?????? ?1 ? ?20 2 + ?????? ?2 ? ?21 2 ????? ??20 = ?????? ?1 ? ?20 ? ?1 + 0 ?20 = ???????(?20) ??20 ??20 = ??????? ?20 ? 1 ? ??????? ?20 ?20 = ?10 (1) ?10 + ?20 (1) ?20 ??20 ??10 (1) = ?10 + 0 ?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10 ?? ?10 (1) ? ????? ????? ??20 ??20 ??20 ??20 ??10 (1)
  • 33. ?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10 ????? ??10 (1) = ????? ??20 ??20 ??20 ??20 ??10 (1) = ? ?????? ?1 ? ?20 ? ??????? ?20 ? 1 ? ??????? ?20 ? ?10 = ? 0.3 ? 0.609 ? 0.609 ? 1 ? 0.609 ? 0.518 = 0.0381 ?10 1 + = ? ? ? ? ????? ??10 1 = 0.4 ? 0.5 ? 0.0381 = 0.380 ?10 1 ? ?? ??? ??? ??? 0.0381, ??? ?10 1 + ?? 0.380
  • 34. ? ?? ?? ? ?? ??? ?? ?????
  • 35. ?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ?10 (0) ? ?10 (1) ? ?? ? ?, ? ? ??? ? ??? ????
  • 36. ?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ??? ???? ?? ? ? ?1? ? ?2 ? ?? ???? ???
  • 37. ?10 (0) ?1 0.5 0.15 0.075 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.518 0.524 0.207 0.209 0.236 0.288 0.609 0.633 0.3 0.9 ????? ??10 (0) = ????? ??10 ??10 ??10 ??10 ??10 (0) ???? = ? ?1 + ? ?2
  • 38. ?10 (1) ?1?20 ?20 0.4 0.207 0.609 0.3 0.518 ?10 ?10?10 (0) ?1 0.5 0.15 0.075 ????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??1 ??10 = ??1 ??20 ??20 ??20 ??20 ??10 = ? ?????? ?1 ? ?20 ? ??????? ?20 ? 1 ? ??????? ?20 ? ?10 (1) = ? 0.3 ? 0.609 ? 0.609 ? 1 ? 0.609 ? 0.4 = 0.0294 # ?10 ? ?1 ? ??? ??? 0.0294
  • 39. ?11 (1) ?2?21 ?21 0.5 0.209 0.621 0.9 0.518 ?10 ?10?10 (0) ?1 0.5 0.15 0.075 ????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??2 ??10 = ??2 ??21 ??21 ??21 ??21 ??10 = ? ?????? ?2 ? ?21 ? ??????? ?21 ? 1 ? ??????? ?21 ? ?11 (1) = ? 0.9 ? 0.621 ? 0.621 ? 1 ? 0.621 ? 0.5 = ? 0.0328 # ?10 ? ?2 ? ??? ??? -0.0328
  • 40. ????? ??10 (0) = ( ??1 ??10 + ??2 ??10 ) ??10 ??10 ??10 ??10 (0) ??2 ??10 = ?0.0345 ??1 ??10 = 0.0305 ??10 ??10 = ??????? ?10 ? 1 ? ??????? ?10 = 0.0294 ? 0.0328 ? 0.249 ? 0.5 = ?0.00042 ?10 0 + = ? ? ? ? ????? ??10 0 = 0.15 ? 0.5 ? (?0.00042) = 0.1502 ??? ?10 0 + ? ?? 0.1502 # ?10 (0) ? ???? ? ??? ??? -0.00042
  • 41. ?10 (0) ?11 (0) ?1 0.5 0.15 0.2 0.075 0.095 ?11 ?11 ?10 (1) ?20 (1) ?1 ?21 ?21 ?20 ?20 ?11 (1) ?21 (1) ?2 ?10 ?10 0.4 0.5 0.45 0.55 0.519 0.524 0.197 0.131 0.248 0.276 0.581 0.628 0.3 0.9 0.380 0.1502 0.250 0.527 0.478 0.1909 ???? = 0.087 ★ 0.076 ?? 1? ?? ?? 13% ??
  • 42. ? ?? ??? ??? Loss ?? ????
  • 43. ? ??? ??? ?? ? ?? ?? ? ?? ?? ? ? ????
  • 44. ? ??? ??? ????
  • 45. ??? ??? ? ????? ?? 1. ??? ??? ?? 2. ??? ??? ?? 3. ???? ????
  • 46. ???????(?) = 1 1+??? ?＞ (?) = 1 1+??? ( 1 ? 1 1+??? ) (0 , ?] C ??? ?, 0 ?? ?? Sigmoid ? ??? ??
  • 47. ?1 ?1 ?Input Sigmoid Function Sigmoid Function Sigmoid Function ?2 ?3 ?1 ?1 ?2 ?2 ?3 ?3 ?1 = ?1 ? ?1 ?2 = ?1 ? ?2 ?3 = ?2 ? ?3 ?1 = ???????(?1) ?2 = ???????(?2) ?3 = ???????(?3) ? = ?3 # Sigmoid ? ?? ???? ???? ?? 0.659 ? ???? ?? ??
  • 49. ?? ??3 = ?? ??3 ??3 ??3 ??3 ??3 = 1 ? ???????＞ ?3 ? ?2 ?? ??2 = ?? ??3 ??3 ??2 ??2 ??2 ??2 ??2 = 1 ? ???????＞ ?3 ? ?3 ? ???????＞ ?2 ? ?1 ?? ??1 = ?? ??2 ??2 ??1 ??1 ??1 ??1 ??1 = 1 ? ???????＞ ?3 ? ?3 ? ???????＞ ?2 ? ?2 ? ???????＞ ?1 ? ?1 ? ?1 ? ?2 ?3 ?3 ?3 ?2 ?2 ?1 ?1 ?3 = ???????(?3) ?3 = ?2 ? ?3 ? = ?3 ?2 = ???????(?2) ?2 = ?1 ? ?2 ?1 = ???????(?1) ?1 = ? ? ?1
  • 50. ?＞ (?) : (0 , ?] C ??? ?, 0 ?? ?? S¨ ? ???? ? ?? ??? ?? ? ?? ? ? ??? ????? 3? ?? S¨ ? ?? ?? ?? ?? ? ?? 0 ? ?? ??? ????? ???
  • 51. ??? ?? : Tanh Function ????(?) = ?2?+ 1 ?2??1 ?＞ (?) =1 ? tanh2(x) (0 , 1] C ??? 1, 0 ?? ??
  • 52. ??? ?? : Tanh Function ?＞ (?) =1 ? tanh2(x) (0 , 1] C ??? 1, 0 ?? ?? ? ?? Tanh ? Sigmoid ? ???? ??? ???? ???? 1??? ??? ??? ? ???
  • 53. ??? ?? : ReLU Function ????(?) = max 0, ? Rectified Linear Unit ?＞(?) = 1 (? − 0 ) 0 (? < 0 ){
  • 54. ??? ?? : ReLU Function ?＞(?) = 1 (? − 0 ) 0 (? < 0 ){ ReLU ? ??? ??? ???? 0, 1 ?? ? ???? ??? ??? ??? ? ???? ?? ????? ?? ?????
  • 55. ???? PReLU, Leaky ReLU, SoftPlus ? ??? ?? ?? ???? ??? ??? ??? ????? ???
  • 56. ???? ???? ?? ?? ?? ?????
  • 57. ???? ??? ??? ?? ?? ???? ????? ??? ??? ??????
  • 58. ??? ???? ???? ????? ?? Xavier ? He initialization ? ?????
  • 59. ??? ??? ???? Activation Function Initialization Code Sigmoid Xavier np.random.randn(n_input, n_output) / sqrt(n_input) ReLU He np.random.randn(n_input, n_output) / sqrt(n_input / 2) 1. Gaussian ?? ??? ?? 2. ????? ????? ??? Xavier Initialization 3. ????? ??? ????? ??? He Initialization
  • 60. ??? ??? ???? Activation Function Initialization Code Sigmoid Xavier np.random.randn(n_input, n_output) / sqrt(n_input) ReLU He np.random.randn(n_input, n_output) / sqrt(n_input / 2) Sigmoid ??? ? Xavier , ReLU ??? ? He ? ???? ????
  • 61. ??? ???? ???? ????? ??? ??? ??? ???? ???? ???? ??? ????
  • 62. ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ??? Input ?? ??? ???? ??? ???? Output ?? ??? ???? ???? ????
  • 63. ?? ??? ???? ??? ? ????? ?? ?? ?????? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
  • 64. ?? ???? ???? ???? ???? ?? ??? ??? ? ???? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
  • 65. ??? ???? ?? ??? ??? Output ?? ??? ? ? ????? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
  • 66. ??? ???? ?? ???? ?? ??? ??? ?? ???? ? ????? ??? ? ?1 ?1 ＞ ?2 ?3 ??? ???＞ ?2 ＞ ?3 ＞ ?? ?? ??
  • 67. Batch Normalization ? Training ?? ??? ???? ?? Vanishing Gradient ? ???? ???? ????? ? ? ????
  • 68. ?? ??? ? ??? ????? ? ??? ???
  • 69. ? ??? ??? ? ?????
  • 71. ???? ??? ??? ?? ? ???? ?? ??? ?? ? ??? ??? ???? 10000 Data Gradient Descent Stochastic Gradient Descent 2500 Data 2500 Data 2500 Data 2500 Data
  • 73. ?? ???? ??? ??? ?? ????
  • 74. ?+ = ? ? ? ? ?? ?? ???? ?? ? ?? ??? Learning Rate ? Gradient ?? ? ???? ??? ??????
  • 75. SGD ?? ?? Gradient ? Learning rate Momentum NAG Adam Adagrad RMSProp AdaDelta Nadam GD ?? : ??? - ???? ???? ???, ???? ??? ??????. ?? ???? ??? ? ??? ?? ??? ???? ??? ? ?? ??? ?? ?? ??? ???? ? ?????? ??? ?????? ?? ??? ? ??? ???? ??? ??? ??? ???? ??? ???? ???? ?????? ?? ???? ??? ??? ?? ?? ???? ???? ??? ?? ??? ??? gradient, learning rate ? ? ???? ??? ?? Adam ?? Momentum ?? NAG ? ???? Nesterov Accelerated Gradient
  • 76. ?? Optimizer ? ??? ?? ?? gradient learning rate
  • 77. ??? ? ?? ??? ?????
  • 82. ??? ????? ???? ? ?? ??? ?????
  • 83. - 1 to 0 : 10?- 0 to 1 : 10? MLP ? ??? ? ??? ????? ???? ???? 20?
  • 84. ?? ????? ???? ???? ???? input ??? ??
  • 86. ??? ? ?? ?? : ??, ??, ??, ??
  • 87. CNN ? ??? ???? ??? ???????
  • 88. 1?? : ??, ????, ??, ????
  • 89. 2?? :?, ?, ?, ?
  • 91. 64 X 64 Convolution Layer 32 X 32 Pooling Layer 32 X 32 Convolution Layer 16 X 16 Pooling Layer Fully-Connected layer 64 X 64 Input CNN ?? ??
  • 93. ?? ??? ??? CNN ??? ?? ??? 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  • 94. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 2 1 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 0 3 1 1 1 1 1 1 1 1 1 1 3 0 0 0 0 0 0 3 0 0 1 0 0 0 0 1 0 0 3 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 0 0 2 1 0 1 1 1 1 1 1 0 1 2 0 0 0 0 0 0 3 0 0 1 2 1 2 2 1 0 0 3 0 0 0 0 0 0 3 0 0 2 1 2 1 1 2 0 0 3 0 0 0 0 0 0 3 0 0 1 1 1 1 1 1 0 0 3 0 0 0 0 0 0 3 0 0 1 0 1 0 0 1 0 0 3 0 0 0 0 0 0 3 0 0 1 1 0 0 0 0 0 0 3 0 0 0 0 0 0 3 0 0 2 2 0 0 0 0 0 0 3 0 0 0 0 0 0 2 1 0 2 2 0 0 0 0 0 0 3 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 0 0 3 1 1 1 1 1 3 0 0 3 1 1 0 1 1 3 0 0 1 1 1 0 1 1 1 0 0 0 1 2 0 2 1 0 0 0 1 1 1 1 1 1 1 0 0 3 1 2 2 2 1 3 0 0 3 0 2 2 2 0 3 0 0 3 0 1 1 1 0 3 0 0 3 1 2 0 0 0 3 0 0 1 1 1 0 0 1 2 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Convolution Max Pooling Convolution : ?? ??
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  • 101. ??? ??? ?? ?? ??? ? ??? ? ?? ?? ?? Pooling?
  • 102. CNN ? ??? ??? ??????
  • 103. ?+ = ? ? ? ? ?? ?? MLP ? ?????
  • 105. 12 X 12 Convolution Layer 6 X 6 Pooling Layer 4 X 4 Convolution Layer 2 X 2 Pooling Layer 4 X 1 Fully-Connected layer 15 X 15 Input 4 ? 4 ??????????? ?????? 2 ? 2 ?????????? ???? 3 ? 3 ??????????? ?????? 2 ? 2 ?????????? ???? ? ? 1 ? ? + 1 ?, ? +2
  • 106. ??? ????? ??????? 3 ? 3 ??????????? ??????
  • 107. ?, ? +2? + 1 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?+1 ? ?? Cost ? ?? ??? ???? ??? ??? ??????
  • 108. ?, ? +2? + 1 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?? ?+1 ? ??? ?? ????? ? ?????
  • 109. ?, ? +2? + 1 ? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1
  • 110. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ? ? Fully Connected Layer ? ?? ? ?? ? ???
  • 111. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?＞ ?＞ ?+1 ? MaxPooling Layer ? ?? ? ? ???
  • 112. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?00 ?+1 ? MaxPooling Layer ? ??? ?? ??? ??? ???? ??? ???
  • 113. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?? ?+1? ? ?? ?+1? ??? ??? ??? ? ?? ???? ?? ????? ??? ??? ?? ????? ?????
  • 114. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?? ?+1? ?? ?＞ ?＞ ? ? 3 ? 3 ???? ????? ? ??? ? ?? ????
  • 115. ?, ? +2? + 1 ? ?? ?+1 = ?(? ?+1 ) ｜ ? ? = ? ?? ?? ?+1 = ?? ?? ? ?? ? ?? ? ?? ? ? ?? ?+1 ? ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ?? ?+1 ? ? = ? ? ? ? ?+2 ?? ? ?+1 ?? ?＞ ?＞ ?+1 = ??? ??(? ?? ?+1) ?? ?00 ?+1 = ?00 ?+1 , ?01 ?+1 , ?10 ?+1 , ?11 ?+1 ? ?? ?+1 = ? ?=0 ??1 ? ?=0 ??1 ? ?? ?+1 ?(?+?)(?+?) ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ? ?+1 ? ????? ? ?+1 ? Cost ? ??? ??? ???? ?? ?? ?+1 ??? ???
  • 116. ?, ? +2? + 1 ?? ?? ?+1 = ?? ?? ? ? ?? ? ? ?? ? ? ?? ? ? ? ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ? ? ? ?? ?＞ ?＞ ?+1 = ? σ ?＞ ?＞ ? ?＞ ?＞ ?+2 ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 = ? ?＞ ?＞ ?+2 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 = ?max[? ?+1] ?? ?? ?+1 = 1, ?? ? ?? ?+1 = max(?) { 0, ????????? ?? ?? ?+1 ?? ?? ?+1 = ? σ ? σ ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? ?? ?? ?+1 = ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? = ?????? ? ? ? ? ? ?1 ?? ?? ? ? 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?＞ ?＞ ? ? ?? ?+1, ? ?? ?+1 ?? ?? ?+1 ? ??? ???? ??? ???? ??? ??? ???? ?? ???
  • 117. 2 ? 2 ?????????? ???? ????? ? ?＞, ?＞ ?, ? +2? + 1 ? ? ?? ?＞ ?＞ ?+1 1 ? 4 ｜ ? ?+2 ?????? ?????? 3 ? 3 ｜ ? ?+1 ??????????? ?????? ????? ? ?, ? ?? ?? ?+1 = ?? ?? ? ? ?? ? ? ?? ? ? ?? ? ? ? ?? ?＞ ?＞ ?+1 ? ?? ?＞ ?＞ ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?? ?+1 ?? ?＞ ?＞ ? = ? ?????? ? ? ? ? ? ? ?＞ ?＞ ?+2 ? ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ? ? ?? ?+1, ? ?? ?+1 ?+ = ? ? ? ? ?? ?? = ? + ? ? ?????? ? ? ? ? ? ? ?＞ ?＞ ?+2 ? ? ? ? ? ? ?? ?+1 ??(?＞+?)(?＞+?) ?
  • 118. ?? ??? Filter ? ?????? ????´ ??? ???????
  • 119. VGG, GoogleNet, ResNet ? ?? ???? ?? ??? ??? ??? ???? ?? ???? ???? ???
  • 120. # Layers ?? ??? ?? ??? ??? ?? ??? ???? ????? ???
  • 122. ?? ??? ?? ??? ???
  • 125. ? ???? ?? ?? ??? ?? ?????
  • 126. ???? ?? ??? ???? CNN ?? ?????
  • 127. Detect Model : OverFeat NewYork Univ - 2014 ??? ????? ?? ??? ???? ?? ???? ????
  • 128. Detect Model : OverFeat NewYork Univ - 2014 ??? ????? ???? ??? ??? ?? ??? ?? ??
  • 129. Detect Model : OverFeat NewYork Univ - 2014 ??? ??? ??? ???? ??
  • 130. Detect Model : OverFeat NewYork Univ - 2014 ?? ??? ???? ????? ???
  • 131. ??? OverFeat? ??? ?? ?? ??? ?? ?? ????
  • 132. ?? ?? ??? ?? ?? CNN ?? ?????
  • 133. Detect Model : R-CNN UC Berkeley - 2014 Input Region Proposals (Selective Search) classifier Compute Regions DOG CAT Classify # ??? ?? ?? ??? ??
  • 134. OverFeat ??? ????? ??? ??? ??? ???? ??
  • 135. R-CNN ?? ?? ??? ?? Region proposal, Classifier ? ??
  • 137. Detect Model : Fast R-CNN Microsoft - 2015 Region Proposals (Selective Search) Compute Regions classifier Classify DOG CAT convolution & pooling R-CNN ? ??? ?? ?? ?? ?? ?? ?? ??
  • 138. Convolution, Pooling ?? ?? ???? ?? ?? Fast R-CNN
  • 140. Detect Model : Faster R-CNN Microsoft - 2015??? Region Proposal ??? ??? Region Proposal Network ?? ?? Region Proposal Network
  • 141. R-CNN Fast R-CNN Faster R-CNN Time per image 50 secs 2 secs 0.2 secs SpeedUp 1x 25x 250x mAP 66.00% 66.90% 66.90% R-CNN benchmark Faster R-CNN ? ?? ?? 5 frame ? ??? ????? Real-Time Image Detection ? ???? ????
  • 142. ???? Yolo, SSD ? ??? ???? ??? ?? ?? ?? ????
  • 143. Image Detection ? ??? ?? ??? ???? ????
  • 144. ?? ??? ?? ??? ? ??? ?? ??? ?? ? ????? ???
  • 145. ???? ???? ??? ?? ?? ??? ??? RNN, Gan ? ?????´ gomguard.tistory.com
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