This is a repository for my work on the paper "Oracle Guided Image Synthesis with Relative Queries".

Paper: https://arxiv.org/abs/2204.14189

OpenReview: https://openreview.net/forum?id=rNh4AhVdPW5

Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow a user (oracle) to generate an image they are envisioning in their head by answering a sequence of relative queries of the form "do you prefer image a or image b?" Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.

If you found this paper interesting please cite using the following bibtex:

@inproceedings{
  helbling2022oracle,
  title={Oracle Guided Image Synthesis with Relative Queries},
  author={Alec Helbling and Christopher John Rozell and Matthew O'Shaughnessy and Kion Fallah},
  booktitle={ICLR Workshop on Deep Generative Models for Highly Structured Data},
  year={2022},
  url={https://openreview.net/forum?id=rNh4AhVdPW5}
}

Create a conda environment from the requirements.txt file.

    conda create --name <env> --file requirements.txt

You can run one of our experiment templates as follows. Each contain a python dictionary, which configures the model, dataset, and experiment.

1. cd auto_localization/experiments/morpho_mnist
2. python bayesian_triplet_experiment.py <run_name>

You can analyze these models using the jupyter notebooks in auto_localization/experiments/morpho_mnist/experiment_analysis/.