1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network. 2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients. 3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.

1. TL-GAN
Transparent latent
cdh.

2. TL GAN in one sec
Matching feature axes to latent space
Without NN finetuning!

3. Why we need TL-GAN?
Matching feature axes to latent space
Matching options(Renderer’s) to feature vector(Director’s)

4. GAN?
http://jaejunyoo.blogspot.com/2017/01/generative-adversarial-nets-2.html
latent vector를 given dataset 분포와 매칭시키는 것

5. In short
Z는 eigenvector / Generator는 eigenvalue
Just for easy understanding…

6. Generating with condition
Style-transfer networks (Pix2Pix, CycleGAN, Stargan)
- Required label & Image
- Hard to control each conditional volume
Conditional Generator (conditional GAN)
- Required label for training
- Need retraining

8. TL-GAN

9. TL-GAN
1h + (generating 8h) : extract image label

10. TL-GAN

11. TL-GAN Discover Correlation
1. Z와 Label간의 coefficient (2D) 를
다변량 다중 선형 회귀를 통해 구한다. 그리고 각 계수를 normalize
https://github.com/SummitKwan/transparent_latent_gan/blob/04439c24ec5b9
d2da458bb933c6a7296d6bed9dd/src/tl_gan/script_label_regression.py

12. TL-GAN Discover Correlation
2. Vector들을 orthogonal하게 조정

13. Before orthogonal

14. After orthogonal

15. 다른 성질을 가지는 vector를
어떻게 맞추었을까?
Unlabeled 데이터에 대해 label를 붙이기 위한 노오력

16. DANN
DANN :
Domain
Adversarial
neural net
training과 test 분포들이 서로
약간 다른(presence of shift) 환경

17. DANN

18. GirlsMoe

19. GirlsMoe – similar approach with tl-gan
Label with pre-trained model (illustration2vec)
To ALL training data & Train C-GAN

20. 참고(보면 좋은 것들)
https://www.slideshare.net/ssuserafc864/domain-adaptation-gan
http://jaejunyoo.blogspot.com/2017/01/domain-adversarial-training-of-
neural-3.html
http://jaejunyoo.blogspot.com/2017/01/generative-adversarial-nets-2.html
https://github.com/SummitKwan/transparent_latent_gan