filter-generation

This repository is organised as follows:

[baselines]:
[data_processing]: All the code to generate data for the filters resides here. Run data_production.py to generate filter data and save it locally.
[modeling]: This folder contains all the code to train the generative models (e.g. VAE, GAN, GMM, etc.) required to run experiments. _joint refers to a joint modeling technique. Within each file, modify the filterpath and savepath according to where your filters are stored, and where you wish to store model checkpoints.
[experiments]: This folder contains code to run downstream tasks on the MNIST dataset, and there exists a 1-1 correspondence (almost) between the files present here and in the modeling section. Load the trained model (stored in loadpath), and run experiments. Results will be saved in a pickle file in savepath.
[visualization]: This folder contains code (in jupyter notebooks) to generate filter samples and histograms for each approach.

manleyroberts / filter-generation