This is an official TensorFlow implementation of GAN-based Priors for Quantifying Uncertainty work. Here, we provide code for our models, checkpoints (to be added) and dataset used to produce results of the paper.

Bayesian inference is a powerful method used extensively to quantify the uncertainty in an inferred field given the measurement of a related field when the two are linked by a mathematical model. Despite its many applications, Bayesian inference faces two major challenges:

1. sampling from high dimensional posterior distributions and
2. representing complex prior distributions that are difficult to characterize mathematically.

In this work we demonstrate how the approximate distribution learned by a generative adversarial network (GAN) may be used as a prior in a Bayesian update to address both these challenges.

We demonstrate the efficacy of this approach by inferring and quantifying uncertainty in inference problems arising in computer vision and physics-based applications. In both instances we highlight the role of computing uncertainty in providing a measure of confidence in the solution, and in designing successive measurements to improve this confidence.

Following animation shows how the proposed method can be used in active learning/design of experiments setting in deciding optimal sensor placement location:

CelebA	MNIST

Following figures demonstrate the effectiveness of the proposed method (in inferring field and quantifying uncertainty) for various inverse problems arising in computer vision and physics-based applications:

• Python 3.7
• Numpy
• TensorFlow 1.14.0
• TensorFlow Probability 0.7.0
• Scipy

After cloning the repo, first change directory to the dataset of interest and follow the steps below.

• CelebA:

  $ python data_celeba.py --download_path data
  

  The above command will download the CelebA dataset and split it into training and test set and store it in the directory specified by --download_path argument.

• MNIST:

  Run trainer.py script as explained below. It will automatically download MNIST dataset and split it into training and test set before proceeding to training.

The proposed algorithm works in two steps: In first step, we train a GAN on a particular dataset to learn the prior distribution and in second step depending upon task at hand, we perform different inference steps.

$ python trainer.py --epoch 100 --z_dim 100 --train_batch_size 64 --lr 0.001

• Selected arguments (see config.py for more details):
  • Hyperparameters
    • --n_critic: no. of discriminator updates for every generator update
    • --train_batch_size: batch size during training (note that this is different than the batch size during posterior inference)
    • --epoch: the max training epochs
    • --lr: learning rate
  • Logging
    • --log_freq: the frequency of printing out log info (default 1)
    • --save_freq: the frequency of saving a checkpoint (default 1000)
    • --sample_freq: the frequency of performing testing inference to save generated fake images during training (default 100)
  • --gpu_id: id of the gpu to be used for training
  • --prefix: a nickname for the training (note that the name of folders will be based on this prefix. To avoid overwriting use different prefix for different runs).

We propose different methods for probing posterior distribution in efficient way for different inference tasks.

1. Image recovery/denoising:

   Here the goal is to infer the true field and associated uncertainty from a noisy measurement.

   $ python mcmc_sampler.py --digit 5 --noise_var 0.1
   $ python mcmc_stats.py --digit 5 --noise_var 0.1
   

   • Selected arguments (see config.py for more details):
     • --digit: an MNIST digit of interest (choose from [0,1,2,..,9])
     • --noise_var: variance of additive Gaussian noise in measurement
     • --n_mcmc: no. of MCMC samples
     • --batch_size: batch size for MCMC inference (should always be one)

   For CelebA, use --img_no argument instead of --digit (see config.py for details).

2. Image inpainting:

   Here the goal is to infer the true field and associated uncertainty from a noisy and occluded measurement.

   $ python inpaint_sampler.py --digit 5 --noise_var 0.1
   $ python inpaint_stats.py --digit 5 --noise_var 0.1
   

   • Selected arguments (see config.py for more details):
     • --start_row: row index of top left corner of mask
     • --end_row: row index of bottom right corner of mask
     • --start_col: column index of top left corner of mask
     • --end_col: column index of bottom right corner of mask

   For CelebA, use --img_no argument instead of --digit (see config.py for details).

If you find this useful, please cite

@article{Patel2020b,
archivePrefix = {arXiv},
arxivId = {2003.12597},
author = {Patel, Dhruv V. and Oberai, Assad A.},
doi = {10.13140/RG.2.2.28806.32322},
title = {{GAN-based Priors for Quantifying Uncertainty}},
url = {https://arxiv.org/abs/2003.12597 https://dx.doi.org/10.13140/RG.2.2.28806.32322},
year = {2020}
}