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1. Spatial Transformer Networks
April 15, 2021
HeeDae Kwon

2. contents
Introduction
Spatial Transformers
Experiments

3. Introduction
Spatial tranformer
- dynamic mechanism
- back-propagation
- select most relevant regions of images
Pooling
- pixed local

4. Spatial Transformers
• Describe the formulation of a spatial transformer
- First, a localisation network
- Second, smapling grid
- Third, image Sampling

5. Localisation Network
• Input feature map :
• Output :
• Need regression layer to produce the tranformation parameters

6. Parameterised Sampling Grid
Attention transformation
Affine transformation

7. Differentiable Image Sampling

8. Differentiable Image Sampling

9. Differentiable Image Sampling

10. Experiments
• Distorted MNIST
• Street View House Numbers
• Fine-Grained Classification

11. DIstorted
MNIST
• Distorted way
- Rotation (R)
- Rotation, scale and tranlsation (RTS)
- Projective transformation (P)

12. Street View House Numbers
• Dataset : Street View House Numbers(SVHN)

13. Street View House Numbers
• Dataset : Street View House Numbers(SVHN)

14. Fine-Grained Classification
• Dataset : Training images Test images Species of birds
6K 5.8K 200

15. • 감사합니다