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Generative Models and Adversarial Training (D3L4 2017 UPC Deep Learning for Computer Vision)

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https://telecombcn-dl.github.io/2017-dlcv/

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.

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Generative Models and Adversarial Training (D3L4 2017 UPC Deep Learning for Computer Vision)

  1. 1. [course site] #DLUPC Kevin McGuinness kevin.mcguinness@dcu.ie Research Fellow Insight Centre for Data Analytics Dublin City University Generative models and adversarial training
  2. 2. What is a generative model? ϴ ● ● ○ ○ P(X = x) x x 2
  3. 3. Why are generative models important? ● ● ● ● ● ● ● 3
  4. 4. Generative adversarial networks ● ● 4
  5. 5. Generative adversarial networks (conceptual) Generator Real world images Discriminator Real Loss Latentrandomvariable Sample Sample Fake 5
  6. 6. The generator 6
  7. 7. The discriminator conv conv ... F F 7
  8. 8. Training GANs Generator Real world images Discriminator Real Loss Latentrandomvariable Sample Sample Fake Alternate between training the discriminator and generator Differentiable module Differentiable module 8
  9. 9. Generator Real world images Discriminator Real Loss Latentrandomvariable Sample Sample Fake 1. Fix generator weights, draw samples from both real world and generated images 2. Train discriminator to distinguish between real world and generated images Backprop error to update discriminator weights 9
  10. 10. Generator Real world images Discriminator Real Loss Latentrandomvariable Sample Sample Fake 1. Fix discriminator weights 2. Sample from generator 3. Backprop error through discriminator to update generator weights Backprop error to update generator weights 10
  11. 11. Training GANs ● ● 11
  12. 12. Discriminator training Generator training 12
  13. 13. Some examples of generated images… 13
  14. 14. ImageNet Source: https://openai.com/blog/generative-models/ 14
  15. 15. CIFAR-10 Source: https://openai.com/blog/generative-models/ 15
  16. 16. Credit: Alec Radford Code on GitHub 16
  17. 17. Credit: Alec Radford Code on GitHub 17
  18. 18. Issues ● ● ● ● 18
  19. 19. Conditional GANs ● ● 19
  20. 20. Generating images/frames conditioned on captions 20
  21. 21. Predicting the future with adversarial training 21
  22. 22. Mathieu et al. Deep multi-scale video prediction beyond mean square error, ICLR 2016 (https://arxiv.org/abs/1511.05440) 22
  23. 23. Image super-resolution (Ledig et al. 2016) 23
  24. 24. Image super-resolution (Ledig et al. 2016) 24
  25. 25. Image-to-Image translation Generator Discriminator Generated pairs Real World Ground truth pairs Loss 25
  26. 26. Questions?

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