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.
Oracle.Guidance.Paper.Video.mp4
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.
cd auto_localization/experiments/morpho_mnistpython bayesian_triplet_experiment.py <run_name>
You can analyze these models using the jupyter notebooks in auto_localization/experiments/morpho_mnist/experiment_analysis/.