Python local-vae package for interpretable models compatible with scikit-learn. Trying to make locally disentangled VAEs.

This repo is actively maintained. For any questions please file an issue.

related work

• TRIM (ICLR 2020 workshop pdf, github) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)
• ACD (ICLR 2019 pdf, github) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy
• CDEP (ICML 2020 pdf, github) - penalizes CD / ACD scores during training to make models generalize better
• DAC (arXiv 2019 pdf, github) - finds disentangled interpretations for random forests
• PDR framework (PNAS 2019 pdf) - an overarching framewwork for guiding and framing interpretable machine learning

reference

• feel free to use/share this code openly
• uses code from disentangling-vae + TRIM
• if you find this code useful for your research, please cite the following:

"""
Python `local-vae` package for interpretable models compatible with scikit-learn.
.. include:: ../readme.md
"""

Sub-modules

src.dsets

src.models

src.style

src.vae