/bmml-ot

Repository for the Bayesian methods of ML course project

Primary LanguageJupyter Notebook

Link to the presentation

Optimal transport maps for distribution preserving operations on latent spaces of Generative Models

Reproducing experiments

  • Prepare GAN models(Faces, MNIST, Rooms) for different generator priors(Normal, Uniform)
  • Implement interpolation functions from the paper:
    • 2-point interpolation
    • n-point interpolation
    • vicinity sampling
    • analogies
  • Conduct experiments and compare results

Experiments with VAE

  • Prepare VAE models(Faces, MNIST, Rooms)
  • Implement functions from the paper:
    • 2-point interpolation
    • n-point interpolation
    • vicinity sampling
    • analogies
  • Implement new interpolation for latent space of VAE
  • Conduct experiments and compare results

Working with missing values

The idea is to check whether it’s possible to combine 2 pictures with missing parts into 1 good image by first mapping into latent space, performing interpolation and then mapping back using decoder(Using different interpolation techniques). So, for this we will need encoder-decoder architecture. We are planning to use VAE for the moment.

Additional fun stuff(if we have time)

Draw 2 dimensional map of the dataset using vicinity sampling