- Generation by GAN
- Image Generation as Example
- Theory behind GAN
- Issues and Possible Solutions
- Conditional Generation
- Unsupervised Conditional Generation
- Relation to Reinforcement Learning
Basic Idea of GAN
step 1. Fix generator G, and update discrimniator D
step 2. Fix discriminator D, and update generator G
Auto-encoder v.s. GAN
GAN in Depth
- A generator G is a network. The network defines a probaility distribution PG.
Can we use other divergence?
Sebastian Nowozin, NIPS, 2016
Issues and Possible Solutions
How to Tain a GAN?
JS divergence is not suitable
What is the problem of JS divergence?
Tip: Improve Quality during Testing
Mode Collapse & Mode Dropping
- Mode collapse
- Mode Dropping