alisure-fork/bayesian_cnn

Bayes by Backprop implemented in a CNN in PyTorch

Bayesian CNN

We introduce Bayesian convolutional neural networks with variational inference, a variant of convolutional neural networks (CNNs), in which the intractable posterior probability distributions over weights are inferred by Bayes by Backprop. We demonstrate how our proposed variational inference method achieves performances equivalent to frequentist inference in identical architectures on several datasets (MNIST, CIFAR10, CIFAR100), while the two desiderata, a measure for uncertainty and regularization are incorporated naturally. We examine in detail how this measure for uncertainty, namely the predictive variance, can be decomposed into aleatoric and epistemic uncertainties.

One convolutional layer with distributions over weights in each filter

Fully Bayesian perspective of an entire CNN

Results

Results on MNIST and CIFAR-10 datasets with AlexNet and LeNet architectures

If you use the work, please cite the work:

@ARTICLE{2018arXiv180605978S,
       author = {{Shridhar}, Kumar and {Laumann}, Felix and {Llopart Maurin}, Adrian and
        {Olsen}, Martin and {Liwicki}, Marcus},
        title = "{Bayesian Convolutional Neural Networks with Variational Inference}",
      journal = {arXiv e-prints},
         year = 2018,
        month = Jun
}