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Evaluating the Interpretability of Generative Models by Interactive Reconstruction

This repository contains code, models, and data for the CHI 2021 paper "Evaluating the Interpretability of Generative Models by Interactive Reconstruction."

Main Idea

There are now a number of approaches for explaining and evaluating how well humans understand discriminative machine learning models. Discriminative models generally have complicated, high-dimensional inputs (like images, audio waveforms, blocks of text, medical timeseries of labs and vitals, etc), but relatively low-dimensional outputs (e.g. a single 0-1 probability, or maybe a categorical prediction over a fixed number of options). Because of this relatively simple structure, it can often be (comparatively) easy to visualize how discriminative model outputs change with changes in their inputs, or to evaluate whether human users understand that relationship for different models.

However, there is another important category of machine learning models called generative models, which often have the opposite structure: their inputs can be relatively simple (e.g. low-dimensional vectors), while their outputs are more complicated. The purpose of generative models isn't necessarily to predict a particular outcome, but instead to model the underlying, lower-dimensional structure in high-dimensional data. This can be useful in a variety of applications, and it's also possible to train generative models in a wider variety of contexts than discriminative models (since they only require the data, not any additional labels or side-information).

However, there is a relative dearth of research on trying to measure how well human users can understand generative models, or even how to visualize them. Also, what methods there are tend to project representations down to two or three dimensions. Low-dimensional projection can help us (test whether users) understand the global geometry of the data, but it doesn't provide insight into (whether users understand) the full representation—in terms of its original dimensions.

In this paper, we present a method for simultaneously visualizing and evaluating the interpretability of generative model representations. This method is based on the idea of "interactive reconstruction," where human users try to manipulate representation dimensions to reconstruct a target input.

The paper is available here, and you can also try out the actual studies we ran live in your web browser.

Repository Structure

  • src/ contains Python code used for training models and computing metrics
  • data/ contains the Sinelines dataset, which we introduce, and is where the dSprites .npz file should be downloaded
  • quiz/ contains a JavaScript-based web application, built with Svelte and TensorFlow.js, that implements the interactive reconstruction task along with several baselines. It also contains the trained models we used in the study.

Reproducing Results

Running the Quiz

After cloning the repository, run the following commands (assuming you have Node.js installed):

cd quiz
npm install
npm run dev

This will start a local server with the quiz application on port 5000. To get started with a list of models and tasks, visit localhost:5000/manifest.html.

Re-training Models

After cloning the repository and installing Python dependencies, the following commands should let you retrain all the models (except InfoGAN on MNIST; see here for instructions):

# Retrain dSprites
dir=./quiz/public/models/dsprites
python src/train_dsprites.py --output_dir=$dir/ae  --variational=0
python src/train_dsprites.py --output_dir=$dir/vae --variational=1
python src/train_dsprites_supervised.py --output_dir=$dir/gt

# Retrain Sinelines
dir=./quiz/public/models/sinelines
python src/train_sinelines.py --output_dir=$dir/ae  --variational=0
python src/train_sinelines.py --output_dir=$dir/vae --variational=1

# Retrain MNIST
dir=./quiz/public/models/mnist
python src/train_mnist.py --output_dir=$dir/ae5  --K=5  --variational=0
python src/train_mnist.py --output_dir=$dir/vae5 --K=5  --variational=1
python src/train_mnist.py --output_dir=$dir/tc5  --K=5  --variational=1 --tc_penalty=9
python src/train_mnist.py --output_dir=$dir/ss5  --K=5  --variational=1 --tc_penalty=9 --semi_supervised=1
python src/train_mnist.py --output_dir=$dir/ae10 --K=10 --variational=0
python src/train_mnist.py --output_dir=$dir/tc10 --K=10 --variational=1 --tc_penalty=9
python src/train_mnist.py --output_dir=$dir/ss10 --K=10 --variational=1 --tc_penalty=9 --semi_supervised=1

Note that the code has been lightly refactored since the original study for clarity (and models have been given more human-friendly names), but it should behave identically.

Note also that you may face TensorFlow.js conversion compatibility issues if using versions other than those listed in the requirements file, as non-backwards-compatible changes have been made to this process since this project's inception.

Citation

@inproceedings{ross2021evaluating,
  author = {Ross, Andrew Slavin and Chen, Nina and Hang, Elisa Zhao and Glassman, Elena L. and Doshi-Velez, Finale},
  title = {Evaluating the Interpretability of Generative Models by Interactive Reconstruction},
  booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
  year = {2021},
  doi = {10.1145/3411764.3445296}
}