progressive gan discussion

1. Progressive Growing of GANS
for Improved Quality, Stability,
and Variation
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen
ICLR 2018 Oral

2. Method Generate Image Disadvantages
Autoregressive
(Pixel RNN/CNN)
Sharp 1. Slow to evaluate (generate pixel by pixel)
2. Do not have a latent representation (similar to Deconv)
VAEs Blurry Information bottleneck
GANs Sharp 1. Only work on small resolutions
2. Hard to train
Motivation

3. • Higher resolution is easier to tell difference.
• Smaller batch size.
• So Grow G and D progressively
Why it is hard?

5. weight α increases linearly from 0 to 1.

6. Minbatch standard deviation
• Insert a constant feature map
• Compute std and concat.

7. Equalized Learning Rate
• Using (He et al., 2015) initialization

8. Pixelwise feature vector normalization in G
• After every conv layer (In G)
• It is like batch-norm but works on pixel wise
• N is the number of feature maps
• In instance-norm a-mean(a) /std(a)

9. Assessment
• MS-SSIM (Gobal image) (all generated images)
• Proposed method (Local image structure) (generated & real)
• Feature (like Sift).
• Distance (using Wassertein distance Rabin et al. 2011)

10. Details
• Basic loss:
Improved WGAN
• Model:
See Right

12. Related work: Laplacian GAN

13. Generative Multi-Adversarial Networks