Slides for my presentation at the Seminar in Computer Graphics at Vienna University of Technology, 2016

1. Applications of Deep Learning
in Computer Vision
Christoph Körner

2. Outline
1) Introduction to Neural Networks
2) Deep Learning
3) Applications in Computer Vision
4) Conclusion

3. Why Deep Learning?
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Wins every computer vision challenge
(classification, segmentation, etc.)
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Can be applied in various domains (speech
recognition, game prediction, computer vision,
etc.)
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Beats human accuracy
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Big communities and resources
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Hardware for Deep Learning

4. Perceptron (1958)
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Weighted sum of inputs
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Threshold operator

5. Artificial Neural Network (1960)
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Universal function approximator
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Can solve the XOR problem

6. Backpropagation (1982)
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Propagate the error through the network
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Allows Optimization (SGD, etc.)
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Enables training of multi-layer networks

7. Convolution and Pooling (1989)
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Less parameters than hidden layers
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More efficient training

8. Handwritten ZIP Codes (1989)
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30 training passes
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Achieved 92% accuracy

9. What happened until 2011?
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Better Initialization
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Better Non-linearities: ReLU
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1000 times more training data
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More computing power
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Factor 1 million speedup in training time through
parallelization on GPUs

10. Deep Learning
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Conv-, Pool- and Fully-Connected Layers
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ReLU activations
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Deep nested models with many parameters
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New layer types and structures
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New techniques to reduce overfitting
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Loads of training data and compute power
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10.000.000 images
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Weeks of training on multi-GPU machines

11. AlexNet (2012)
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62.378.344 parameters (250MB)
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24 layers

12. VGGNet (2013)
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102.908.520 parameters (412MB)
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23 layers

13. GoogLeNet (2014)
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6.998.552 parameters (28MB)
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143 layers

14. Inception Module
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Heavy use of 1x1 convolutions (applied along the
depth dimension)
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Very efficient

15. ResNet (2015)
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Residual learning
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152 layers

16. Applications in Computer Vision

17. Classification
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One class per image
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Softmax layer at the end

18. Localization
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Bounding box Regression
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Sigmoid layer with 4 outputs at the end
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Via Classification

19. Detection
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Multiple Objects, multiple classes
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Solved using multiple networks

20. Segmentation

21. More Applications
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Compression
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Auto-encoders, Self-organizing maps
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Image Captioning
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Solved with Recurrent Architecture
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Image Stylization
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Clustering
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Many more...

22. Conclusion
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Powerful, learn from data instead of hand-crafted
feature extraction
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Better than humans
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Deeper is always better
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Overfitting
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More data is always better
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Data quality
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Ground truth

23. Thank you!
Christoph Körner