1. Presented by Shixing Chen
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Presented by
Shixing Chen

2. Background
Data augmentation
apply transformation or add noise

3. Background Image synthesis generate desired types of images
I. Goodfellow et al. NIPS, 2014.
Generated CIFAR-10
T. Salimans et al. NIPS, 2016.

4. Style transfer
D. Ulyanov et al. CVPR, 2017.

5. Adaptive Instance Normalization

6. Reason for IN’s effectiveness
A kind of style normalization

7. Training of AdaIN

8. Style and content losses
10 style images, 50 content images

10. Comparison with baselines

11. Speed comparison
256 × 256 and 512 × 512 images

12. Content-style trade-off