The document contains several links to papers and slides about generative adversarial networks (GANs). GANs use two neural networks, a generator and discriminator, that compete against each other in a game theoretic framework. The generator learns to generate new data with the same statistics as the training set to fool the discriminator, while the discriminator learns to determine whether samples are real or generated.

2. http://ieeexplore.ieee.org/abstract/document/7934380/

4. http://www.spect.com/Web-Album/photos/photo_12.html

8. Berthelot, David, Tom Schumm, and Luke Metz. "Began: Boundary equilibrium generative adversarial networks." arXiv preprint arXiv:1703.10717(2017)

10. Jaejun Yoo’s slide (https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo)

11. Jaejun Yoo’s slide (https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo)

12. Jaejun Yoo’s slide (https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo)

13. Jaejun Yoo’s slide (https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo)

14. Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems (2014)

15. Namju Kim’s slide (https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091)

16. Namju Kim’s slide (https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091)

17. Namju Kim’s slide (https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091)

18. Namju Kim’s slide (https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091)

20. FAKE
(low dose)
REAL
(routine dose)
(routine dose)
REAL

24. Smooth signals (low SD) Both learned to reduce the noise level

25. routine
dose
low dose

29. low dose
routine
dose
* A CAC candidate mask is shown, indicating all voxels ≥ 130 HU.