Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

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Deep Learning disentanglement
Overview

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar Carlsson, and Stefano Ermon
Computer Science & Math departments, Stanford University

Read the paper.

Setup

Requirements and environment files are in env/

Run

run_barcodes.py: iterate over various models and datasets to generate their corresponding persistence barcodes
run_vis_cov.py: run vis_cov.py for all datasets and decoder combinations, where vis_cov.py takes saved barcodes and produces W. RLT figures
run_gen_cov.py: run gen_cov.py for all datasets and decoder combinations, where gen_cov.py calculates the similarity matrices between two barcodes, then finds spectrally coclustered biclusters, and finally calculates the scores
parse_scores.py: aggregate the results of existing runs

Other utilities

global_models.py: encoders and decoders of various generative models
utils.py: collection of utilities for sampling, model-loading, conversions

args/: argument parsers/options for different models
datasets/: resources for handling our datasets
env/: requirements files, various pip/conda details
supplement/: scripts for some appendix experiments

External libraries

Geometry score, modified to include Wasserstein features: gs/
Disentanglement VAEs lib: disentangling_vae/
Pytorch GAN zoo: models/
StyleGAN resources: alae/
Various CelebA GAN implementations: celeba_gan/

Deprecated

Some deprecated code referring to transcoder may be present in certain files. This constitutes another set of experiments that we were running in parallel for mapping factors of variation to latents. Thus is research - perhaps future work ;)

Acknowledgements

We would like to thank Torbjorn Lundh and Samuel Bengmark for their helpful feedback and encouragement in the preparation of our manuscript.

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Comments
  • ETA? :-P

    ETA? :-P

    Hi guys!

    Didn't expect to get an issue on an empty repository, did you? :-P Just to be able to schedule future work (decide to evaluate it under your metric + reserving time to do so), when can we expect the code to be up?

    Cheers, Dominik

    opened by dominikzietlow 1
  • Differences between paper and code

    Differences between paper and code

    Hi, While reading your code, I realized some slight differences between your algorithm in the paper and what was implemented here, and found it really difficult to understand, could you help me to clarify?

    • In the paper, barcodes at different values of a factor are combined by Wasserstein barycenter. However, function barymean here computes between different runs of RLT algorithm at a specific value (which are totally different because we are using witness complex). And in this repo barcodes at different values of a factor are aggregated by simple mean operator (as shown in here).
    • The formulation of (unsupervised) evaluation metric is $\mu = \rho_c - \rho_{\c}$, in which $\rho_c$ are summation of W distance of mean barcodes of different factor in a same cluster, and $\rho_{\c}$ are those at different clusters minus $\rho_c$. When I look at bicluster_score function, it seems to be $\rho_{\c}$ only, which makes me really confused.
    opened by nguyenhungquang 0
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