1. Learning Compositional Visual Concepts with Mutual Consistency
Yunye Gong, Srikrishna Karanam, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Peter C. Doerschuk CVPR 2018 (spotlight)
Concept GAN
Presented by: Anand Bhattad (Vision Lunch, April )

2. Image Translation (Pix to Pix and Cycle GAN)
Pix2Pix
Cycle GAN
Source: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial

3. Cycle GAN

4. Source: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial

5. New Unseen Samples from Translations?
Colored Bags
Edge Bags
Colored Shoes
Edge Shoes

6. Overview: Cycle GAN G1 F1 F2 G2 F2 G2 G1 F1
Cycle GAN: Learn and Compose concepts separately G1 F1 G2 F2 G1 F1 G2 F2

7. Losses: Adversarial G1 F1 G2 F2

8. Losses: Cycle Consistency

9. Can we generate sketch shoes by combining them?F1 F2 G2 F2 G2 G1 F1

10. Proposed Model: Concept GAN
Cycle GAN: Learn and Compose concepts separately
Concept GAN: Learn and Compose concepts simultaneously G1 F1 F2 F2 G2 F2 G2 G1 F1

11. Losses: Cycle Consistency

12. Losses: Cycle Consistency (Dist=4)G1 G1 F1 F1 F2 G2 F2 G2

13. Losses: Cycle Consistency (Dist=4)G1 G1 F1 F1 F2 G2 F2 G2

14. Losses: Cycle Consistency (Dist=4)G1 G1 F1 F1 F2 G2 F2 G2

15. Loss: Commutative G1 G1 F1 F1 F2 G2 F2 G2

16. Loss: Commutative G1 F1 F2 G2 F2 G2 G1 F1

17. Loss: Commutative G1 F1 F2 G2 F2 G2 G1 F1

18. Concept GAN

19. Comparing Results

20. Qualitative Results

21. Qualitative Results

22. Quantitative Results (Classification)
Test Set: edge Shoes only
Test Set: with eyeglasses, with bangs 18 60
(Accuracy if L_x removed)

23. Ablation Study

24. Re-ID

25. Generalizing to n(=3) Concepts

26. Learning from 2 attributes in 2 experiments
Experiment1: (smile, eyeglasses) Experiment2: (bangs, eyeglasses) (smile, eyeglasses, bangs)

27. Yaxing Wang, Joost van de Weijer, Luis Herranz CVPR 2018
Mix and match networks: encoder-decoder alignment for zero-pair image translation
Yaxing Wang, Joost van de Weijer, Luis Herranz CVPR 2018

28. Task
Mix and match networks do not require retraining on unseen transformations, in contrast to unpaired translation alternatives (e.g. CycleGAN)
leverages depth and semantic segmentation, ignoring the available RGB data and the corresponding pairs

29. Overview

30. Losses

31. Depth –to- Semantic segmentation

32. Depth-to-Semantic segmentation