Trying to make locally disentangled VAEs.
This repo is actively maintained. For any questions please file an issue.
- 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
- 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: