A Disentangling Invertible Interpretation Network for Explaining Latent Representations

PyTorch code accompanying the CVPR 2020 paper

A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Patrick Esser*, Robin Rombach*, Björn Ommer
* equal contribution

arXiv | BibTeX | Project Page

Table of Contents

• Requirements
• Data
• Training
  • Autoencoders
  • Classifiers
  • Invertible Interpretation Networks
    • Unsupervised
    • Supervised
• Evaluation
• Pretrained Models
• Results
• BibTeX

Requirements

A suitable conda environment named iin can be created and activated with:

conda env create -f environment.yaml
conda activate iin

Optionally, you can then also conda install tensorflow-gpu=1.14 to speed up FID evaluations.

Data

MNIST, FashionMNIST and CIFAR10 will be downloaded automatically the first time they are used and CelebA will prompt you to download it. The content of each dataset can be visualized with

edexplore --dataset iin.data.<dataset>

where <dataset> is one of MNISTTrain, MNISTTest, FashionMNISTTrain, FashionMNISTTest, CIFAR10Train, CIFAR10Test, CelebATrain, CelebATest, FactorCelebATrain, FactorCelebATest, ColorfulMNISTTrain, ColorfulMNISTTest, SingleColorfulMNISTTrain, SingleColorfulMNISTTest.

Training

Autoencoders

To train autoencoders, run

edflow -b configs/<dataset>_ae.yaml -t

where <dataset> is one of mnist, fashionmnist, cifar, celeba, cmnist. To enable logging to wandb, adjust configs/project.yaml and add it to above command:

edflow -b configs/<dataset>_ae.yaml configs/project.yaml -t

Classifiers

To train a classifier on ColorfulMNIST, run

edflow -b configs/cmnist_clf.yaml -t

Once you have a checkpoint, you can estimate factor dimensionalities using

edflow -b configs/cmnist_clf.yaml configs/cmnist_dimeval.yaml -c <path to .ckpt>

For the pretrained classifier, this gives

[INFO] [dim_callback]: estimated factor dimensionalities: [22, 11, 31]

and to compare this to an autoencoder, run

edflow -b configs/cmnist_ae.yaml configs/cmnist_dimeval.yaml -c <path to cmnist ae .ckpt>

which gives

[INFO] [dim_callback]: estimated factor dimensionalities: [13, 17, 34]

Invertible Interpretation Networks

Unsupervised on AE

To train unsupervised invertible interpretation networks, run

edflow -b configs/<dataset>_iin.yaml [configs/project.yaml] -t

where <dataset> is one of mnist, fashionmnist, cifar, celeba. If, instead of using one of the pretrained models, you trained an autoencoder yourself, adjust the first_stage config section accordingly.

Supervised

For supervised, disentangling IINs, run

edflow -b configs/<dataset>_diin.yaml [configs/project.yaml] -t

where <dataset> is one of cmnist or celeba, or run

edflow -b configs/cmnist_clf_diin.yaml [configs/project.yaml] -t

to train a dIIN on top of a classifier, with factor dimensionalities as estimated above (dimensionalities of factors can be adjusted via the Transformer/factor_config configuration entry).

Evaluation

Evaluations run automatically after each epoch of training. To start an evaluation manually, run

edflow -p logs/<log_folder>/configs/<config>.yaml

and, optionally, add -c <path to checkpoint> to evaluate a specific checkpoint instead of the last one.

Pretrained Models

Download logs.tar.gz (~2.2 GB) and extract the pretrained models:

tar xzf logs.tar.gz

Results

Using spectral normalization for the discriminator, this code slightly improves upon the values reported in Tab. 2 of the paper.

Dataset	Checkpoint	FID
MNIST	105600	5.252
FashionMNIST	110400	9.663
CelebA	84643	19.839
CIFAR10	32000	38.697

Full training logs can be found on Weights & Biases.

BibTeX

@inproceedings{esser2020invertible,
  title={A Disentangling Invertible Interpretation Network for Explaining Latent Representations},
  author={Esser, Patrick and Rombach, Robin and Ommer, Bj{\"o}rn},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2020}
}