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01.cnn - 본격CNN논문 파헤치기
- 1. CNN
- 2. CNN ...
- 3. CNN
- 4. TEXT . ▸ Terminology & Notation ▸ Structure model ▸ implementation ▸ Pros and Cons
- 5. TEXT 4 . ▸ AlexNet ▸ VGG ▸ GoogLeNet ▸ ResNet ILSVRC (IMAGENET LARGE SCALE VISUAL RECOGNITION COMPETITION)
- 7. ALEXNET
- 8. ALEXNET WHY ALEXNET ▸ . ▸ . ▸ 1 15.3% 2 26.2%(2 )
- 9. ALEXNET ALEXNET ▸ ReLu(Rectified Linear Unit) ▸ LRN(Local Response Normalization) ▸ Regularization - overfitting ▸ Data augmentation ▸ Dropout
- 10. ALEXNET RELU(RECTIFIED LINEAR UNIT) ▸ ? ▸ Solving Vanishing gradient problem ‣ SPARSE ACTIVATION: 0 0 . ‣ EFFICIENT GRADIENT PROPAGATION: GRADIENT VANISHING GRADIENT EXPLODING . ‣ EFFICIENT COMPUTATION: .
- 11. ALEXNET LRN(LOCAL RESPONSE NORMALIZATION) ▸ Generalization ▸ It implements a form of lateral inhibition inspired by real neurons
- 12. ALEXNET LRN(LOCAL RESPONSE NORMALIZATION) ▸ Generalization ▸ It implements a form of lateral inhibition inspired by real neurons
- 13. ALEXNET REGULARIZATION ▸ Dropout ▸ Data augmentation
- 14. ALEXNET REGULARIZATION ▸ Dropout ▸ Data augmentation
- 16. ALEXNET
- 17. VGG
- 18. VGG LOOKING THROUGH VGG PAPER
- 19. VGG
- 20. VGG ▸ conv 3x3 conv , . 3x3 conv 3 7x7 conv 1 . 3 * (3^2C^2) = 27C^2 // 7^2C^2 = 49C^2 CNN parameter regularization . ▸ LRN . ▸ Validation data AlexNet ▸ Data augmentation image-rescaling .( ) VGG
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- 32. TEXT NEXT TIME ▸ ResNet ▸ Overfitting ▸ Regularization