Normalizing flows

Normalizing flow models are generative models, i.e. they infer the underlying probability distribution of an observed dataset. With that distribution we can do a number of interesting things, namely sample new realistic points and query probability densities

Resources

• https://github.com/janosh/awesome-normalizing-flows
• Flow-based Deep Generative Models
• Normalizing flow models
• http://akosiorek.github.io/ml/2018/04/03/norm_flows.html

Talks

• #TALK Introduction to Normalizing Flows (ECCV2020 Tutorial)

Code

• #CODE Normalizing Flows in JAX
• #CODE NuX - Normalizing Flows using JAX

References

• #PAPER NICE: Non-linear Independent Components Estimation (Dinh 2015)
• #PAPER Glow: Generative Flow with Invertible 1x1 Convolutions (Kingma 2018)
  • https://openai.com/blog/glow/
  • #CODE https://github.com/openai/glow
• #PAPER #REVIEW Normalizing Flows: An Introduction and Review of Current Methods (Kobyzev 2020)

Image-to-image translation

See AI/Computer Vision/Image-to-image translation#Flow-based