Uncertainty estimation via decorrelation and DPP

Code for paper "Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling" by Kirill Fedyanin, Evgenii Tsymbalov, Maxim Panov published in Recent Trends in Analysis of Images, Social Networks and Texts by Springer. You can cite this paper by bibtex provided below.

Main code with implemented methods (DPP, k-DPP, leverages masks for dropout) are in our alpaca library

Motivation

For regression tasks, it could be useful to know not just a prediction but also a confidence interval. It's hard to do it in the close form for deep learning, but to estimate, you can you so-called ensembles of several models. To avoid training and keeping several models, you could use monte-carlo dropout on inference.

To use MC dropout, you need multiple forward passes; converging requires tens or even hundreds of forward passes.

We propose to force the diversity of forward passes by hiring determinantal point processes. See how it improves the log-likelihood metric across various UCI datasets for the different numbers of stochastic passes T = 10, 30, 100.

Paper

You can read full paper here https://link.springer.com/chapter/10.1007/978-3-031-15168-2_11

For the citation, please use

@InProceedings{Fedyanin2021DropoutSB,
author="Fedyanin, Kirill
and Tsymbalov, Evgenii
and Panov, Maxim",
title="Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling",
booktitle="Recent Trends in Analysis of Images, Social Networks and Texts",
year="2022",
publisher="Springer International Publishing",
pages="125--137",
isbn="978-3-031-15168-2"
}

Install dependency

pip install -r requirements.txt

Regression

To get the experiment results from the paper, run the following notebooks

• experiments/regression_1_big_exper_train-clean.ipynb to train the models
• experiments/regression_2_ll_on_trained_models.ipynb to get the ll values for different datasets
• experiments/regression_3_ood_w_training.ipynb for the OOD experiments

Classification

From the experiment folder run the following scripts. They goes in pairs, first script trains models and estimate the uncertainty, second just print the results.

Accuracy experiment on MNIST

python classification_ue.py mnist
python print_confidence_accuracy.py mnist

Accuracy experiment on CIFAR

python classification_ue.py cifar 
python print_confidence_accuracy.py cifar 

Accuracy experiment on ImageNet

For the imagenet you need to manually download validation dataset (version ILSVRC2012) and put images to the experiments/data/imagenet/valid folder

python classification_imagenet.py 
python print_confidence_accuracy.py imagenet

OOD experiment on MNIST

python classification_ue_ood.py mnist
python print_ood.py mnist

OOD experiment on CIFAR

python classification_ue_ood.py cifar 
python print_ood.py cifar 

OOD experiment on ImageNet

python classification_imagenet.py --ood
python print_ood.py imagenet 

You can change the uncertainty estimation function for mnist/cifar by adding -a=var_ratio or -a=max_prob keys to the scripts.