PAPER-REVIEW-0002, ImageNet Classification with Deep Convolutional Neural Networks

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Contents

1. PROLOGUE
2. INTRODUCTION
3. THE DATASET
4. THE ARCHITECTURE
5. REDUCING OVERFITTING
- 5.1. Data augmentation
- 5.2. Dropout
6. DETAILS OF LEARNING
7. RESULTS
- 7.1. Qualitative evaluations
8. DISCUSSION
9. EPILOGUE

1. PROLOGUE

2. INTRODUCTION

3. THE DATASET

4. THE ARCHITECTURE

4.1. Rectified Linear Unit nonlinearity

4.2. Training on multiple GPUs

4.3. Local response normalization

4.4. Overlapping pooling

4.5. Overall architecture

5. REDUCING OVERFITTING

5.1. Data augmentation

5.2. Dropout

6. DETAILS OF LEARNING

7. RESULTS

7.1. Qualitative evaluations

8. DISCUSSION

9. EPILOGUE

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Reference

OUTPUT