Face Translation using CycleGAN

1. KaoNet v2
Face Translation using CycleGAN
October 29, 2017
Van Phu Quang Huy

2. Agenda
1. KaoNet recap
2. New feature: Face Translation
3. Technical Details

3. KaoNet recap: Face Recognition (OpenCV+CNN)

4. KaoNet recap: Face Generation (DCGAN)

5. KaoNet v2 ...

6. New feature: Face Translation
Khang → Huy Huy → Khang
In (real) In (real)Out (fake) Out (fake)

7. New feature: Face Translation
Girl → Khang Huy → Girl
In (real) In (real)Out (fake) Out (fake)

8. Technical Details: CycleGAN

9. CycleGAN
● Paper: https://arxiv.org/abs/1703.10593
● In ICCV 2017 (International Conference on
Computer Vision)
○ October 22-29, 2017
○ @Venice, Italy

10. CycleGAN (https://junyanz.github.io/CycleGAN/)

11. CycleGAN (https://junyanz.github.io/CycleGAN/)

12. CycleGAN (https://junyanz.github.io/CycleGAN/)

13. My Implementation on GitHub!
https://github.com/vanhuyz/CycleGAN-TensorFlow

14. Reference: Generative Adversarial Networks (GAN)
(http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them)

15. Reference: pix2pix (https://phillipi.github.io/pix2pix/)

16. Reference: Neural Style Transfer (arXiv1508.06576)

17. Paired and Unpaired Data

18. CycleGAN Overview

19. GeneratorX2Y
GeneratorY2X
DiscriminatorY
Cycle-consistency loss
GAN loss
G
DY
F

20. Loss Function
● GAN Loss
● Cycle-consistency Loss
● Full Loss

21. Network Architectures

22. Generator
(https://hardikbansal.github.io/CycleGANBlog/)

23. Reference: Residual Block (in ResNet)
(http://icml.cc/2016/tutorials/icml2016_tutorial_deep_residual_networks_kaiminghe.pdf)

24. Reference: Transposed Convolution (a.k.a Deconvolution)
(https://github.com/vdumoulin/conv_arithmetic
)

25. Discriminator
(https://hardikbansal.github.io/CycleGANBlog/)

26. Training Techniques

27. Training Techniques in Generators
● Instance Normalization
● Reflect Padding

28. Training Techniques in Discriminators
● PatchGAN with fully convolutional networks
● Use least square loss instead of cross entropy
● Use history of generated images rather than the
latest ones
● Use LeakyReLU instead of ReLU