- Goal: Distinguish different dark energy models using supernovae data.
- Traditionally done through Bayesian techniques. Cons: time expensive.
- This paper proposes using a VAE-GAN (vegan hereafter) to do model selection.
Data
Vegan = VAE + GAN
Generative Adversarial Network (GAN)
We want to:
- Learn the distribution that generates the data p(x).
- Generate new samples from p(x)
How we do it:
- Two CNNs: Generator G and Discriminator D.
- We train G to generate new "fake" samples to trick D.
- We train D to distinguish fake and real samples.
- Training ends when there is a "stalemate".
Generative Adversarial Network (GAN)
Example
Variational AutoEnoder (VAE)
Loss function:
Vegan = VAE + GAN
Model comp
By arnauqb
