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Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu
[arXiv] [GitXiv] [slides] [video]
Spatial transformer
n...
1.
Introduction
Why do we need spatial transformer networks?
Why do we need
Spatial transformer networks?
Are Convolutional Neural Networks invariant to…
▪ Scale?
▪ Rotation?
▪ Transl...
Why do we need
Spatial transformer networks?
CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
Why do we need
Spatial transformer networks?
Are Convolutional Neural Networks invariant to…
▪ Scale? No
▪ Rotation?
▪ Tra...
Why do we need
Spatial transformer networks?
Are Convolutional Neural Networks invariant to…
▪ Scale? No
▪ Rotation? No
▪ ...
Why do we need
Spatial transformer networks?
Are Convolutional Neural Networks invariant to…
▪ Scale? No
▪ Rotation? No
▪ ...
Why do we need
Spatial transformer networks?
A. W. Harley, "An Interactive Node-Link Visualization of Convolutional Neural...
2.
Spatial transformers
Understanding how they work
Intuition behind
Spatial transformers
Intuition behind
Spatial transformers
Intuition behind
Spatial transformers
Sampling!
Formulating
Spatial transformers
Three main differentiable blocks:
▪ Localisation network
▪ Grid generator
▪ Sampler
Grid generator:
Examples
Affine transform Attention model
- coordinates in the target (output) feature map
- coordinates i...
Sampler:
Mathematical formulation
Generic sampling
kernel
From the grid
generator
All pixels in the
output feature map
3.
Experiments
Evaluating spatial transformer networks
Experiment #1:
Distorted MNIST
Experiment #1:
Distorted MNIST
Distortions: Rotation, Translation, Projective, Elastic
Transformations: Affine, Projective...
Experiment #2:
Multiple spatial transformers
Experiment #2:
Adding two digits in an image
Experiment #2:
Adding two digits in an image
Experiment #2:
Adding two digits in an image
Other experiments:
Applications of spatial transformers
▪ Street View House Numbers
▪ Fine-grained classification
4.
Conclusions
Conclusions:
Spatial transformer networks
▪ A module that performs spatial transformations to features has
been presented
...
Thanks!
Any question?
Spatial transformer networks
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Spatial transformer networks

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Presentation by Victor Campos at the Universitat Politecnica de Catalunya (UPC) of the paper:

Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. "Spatial transformer networks." In Advances in Neural Information Processing Systems, pp. 2008-2016. 2015.

Part of the Computer Vision Reading Group. Full list of papers and schedules:
https://github.com/imatge-upc/readcv

Publié dans : Technologie
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Spatial transformer networks

  1. 1. Max Jaderberg, Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu [arXiv] [GitXiv] [slides] [video] Spatial transformer networks Slides by Víctor Campos [GDoc] Computer Vision Reading Group (28/03/2016)
  2. 2. 1. Introduction Why do we need spatial transformer networks?
  3. 3. Why do we need Spatial transformer networks? Are Convolutional Neural Networks invariant to… ▪ Scale? ▪ Rotation? ▪ Translation?
  4. 4. Why do we need Spatial transformer networks? CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
  5. 5. Why do we need Spatial transformer networks? Are Convolutional Neural Networks invariant to… ▪ Scale? No ▪ Rotation? ▪ Translation?
  6. 6. Why do we need Spatial transformer networks? Are Convolutional Neural Networks invariant to… ▪ Scale? No ▪ Rotation? No ▪ Translation?
  7. 7. Why do we need Spatial transformer networks? Are Convolutional Neural Networks invariant to… ▪ Scale? No ▪ Rotation? No ▪ Translation? Partially
  8. 8. Why do we need Spatial transformer networks? A. W. Harley, "An Interactive Node-Link Visualization of Convolutional Neural Networks," in ISVC, pages 867-877, 2015
  9. 9. 2. Spatial transformers Understanding how they work
  10. 10. Intuition behind Spatial transformers
  11. 11. Intuition behind Spatial transformers
  12. 12. Intuition behind Spatial transformers Sampling!
  13. 13. Formulating Spatial transformers Three main differentiable blocks: ▪ Localisation network ▪ Grid generator ▪ Sampler
  14. 14. Grid generator: Examples Affine transform Attention model - coordinates in the target (output) feature map - coordinates in the source (input) feature map
  15. 15. Sampler: Mathematical formulation Generic sampling kernel From the grid generator All pixels in the output feature map
  16. 16. 3. Experiments Evaluating spatial transformer networks
  17. 17. Experiment #1: Distorted MNIST
  18. 18. Experiment #1: Distorted MNIST Distortions: Rotation, Translation, Projective, Elastic Transformations: Affine, Projective, Thin Plate Spline (TPS)
  19. 19. Experiment #2: Multiple spatial transformers
  20. 20. Experiment #2: Adding two digits in an image
  21. 21. Experiment #2: Adding two digits in an image
  22. 22. Experiment #2: Adding two digits in an image
  23. 23. Other experiments: Applications of spatial transformers ▪ Street View House Numbers ▪ Fine-grained classification
  24. 24. 4. Conclusions
  25. 25. Conclusions: Spatial transformer networks ▪ A module that performs spatial transformations to features has been presented ▪ It is differentiable and learnt in an end-to-end fashion ▪ No modifications to the loss function are needed ▪ Outperforms the state-of-the-art performance in some tasks
  26. 26. Thanks! Any question?

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