InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

[nagataka]

Summary

Link

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• https://github.com/openai/InfoGAN

Author/Institution

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
UC Berkeley, OpenAI

What is this

• Present a simple modification to the generative adversarial network objective that encourages it to learn interpretable and meaningful representations.
  • Maximizing the mutual information between fixed small subset of the GAN's noise variables and the observations

Comparison with previous researches. What are the novelties/good points?

• No supervision of any kind

Key points

z in the GAN. In this paper, they decompose the input noise vector into two parts:

1. z, which is treated as source of incompressible noise
2. c, which we will call the latent code and will target the salient structured semantic features of the data distribution

GAN's minimax game formalization

InfoGAN's formulation where I is a mutual information

How the author proved effectiveness of the proposal?

• Experiments on MNIST, CelebA, and SVHN
• Two goals
  • If mutual information can be maximized efficiently
  • To evaluate if InfoGAN can learn disentangled and interpretable representations
    • Confirmed by making use of the generator to vary only one latent factor at a time in order to assess if varying such factor results in only one type of semantic variation in generated iamges

Any discussions?

What should I read next?

• Representation learning: A review and new perspectives