Use Gaussian processes (GPs) to add uncertainty to convolutional neural networks (CNNs). GPCNN uses pretrained CNNs as feature extractors to train a GP classifier. The uncertainty in the CNN predictions is determined using the variance associated with the prediction made by the GP classifier, as opposed to use the outputs of the CNN to determine the uncertainty directly.
NOTE This implementation is not compatible with the latest version of GPyTorch.
Create a virtual environment, for example using Anaconda:
conda create -n gpcnn python=3.6
Activate the virtual environment:
conda activate gpcnn
Install the requirements:
pip install -r requirements.txt
For all experiments, the hyperparameters can be adjusted in the settings.yaml file associated with each model in the models/ directory.
First train the CNN directly:
python main.py -e mnist -m mnist
Thereafter, train the GP classifier using the pretrained MNIST model as a feature extractor:
python main.py -e mnist_gpcnn -m gpcnn -g mnist
Results can be generated using the following command:
python main.py -e mnist_gpcnn -m gpcnn -g mnist --evaluate
The following are examples of the distributions of the standard deviations associated with each class, when making correct and incorrect predictions:
The following are examples of the variance associated with the probability of a prediction being correct for a specific class:
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