Generative Models and Adversarial Training (D3L4 2017 UPC Deep Learning for Computer Vision)
This document discusses generative adversarial networks (GANs) which use two neural networks - a generator and a discriminator. The generator produces synthetic samples and the discriminator evaluates them as real or fake. They are trained in an adversarial process where the generator learns to produce samples that cannot be distinguished from real data, while the discriminator learns to better detect the generator's synthetic samples. GANs can generate realistic images like faces and objects and have applications such as image super-resolution, future frame prediction in videos, and image-to-image translation tasks. Training GANs requires alternating between updating the discriminator to improve detection and updating the generator to avoid detection.
![[course site]
#DLUPC
Kevin McGuinness
kevin.mcguinness@dcu.ie
Research Fellow
Insight Centre for Data Analytics
Dublin City University
Generative models and
adversarial training](https://image.slidesharecdn.com/dlcv2017d3l4generativemodelsandadversarialtraining-170623173105/85/Generative-Models-and-Adversarial-Training-D3L4-2017-UPC-Deep-Learning-for-Computer-Vision-1-320.jpg)
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Generative Models and Adversarial Training (D3L4 2017 UPC Deep Learning for Computer Vision)
- 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. What is a generative model? ϴ ● ● ○ ○ P(X = x) x x 2
- 3. Why are generative models important? ● ● ● ● ● ● ● 3
- 5. Generative adversarial networks (conceptual) Generator Real world images Discriminator Real Loss Latentrandomvariable Sample Sample Fake 5
- 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. 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. 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
- 13. Some examples of generated images… 13
- 16. Credit: Alec Radford Code on GitHub 16
- 17. Credit: Alec Radford Code on GitHub 17
- 20. Generating images/frames conditioned on captions 20
- 21. Predicting the future with adversarial training 21
- 22. Mathieu et al. Deep multi-scale video prediction beyond mean square error, ICLR 2016 (https://arxiv.org/abs/1511.05440) 22
- 23. Image super-resolution (Ledig et al. 2016) 23
- 24. Image super-resolution (Ledig et al. 2016) 24
- 25. Image-to-Image translation Generator Discriminator Generated pairs Real World Ground truth pairs Loss 25
- 26. Questions?