Exploring different ways of describing uncertainty in neural networks is of great interest. Artificial intelligence models can be used with greater confidence by having solid methods for identifying and quantifying uncertainty. This is especially important in high-risk areas such as medical applications, autonomous vehicles, and financial systems. This thesis explores how to detect classification outliers in Bayesian Neural Networks. A few methods exist for quantifying uncertainty in Bayesian Neural Networks, such as computing the Entropy of the prediction vector. Is there a more accurate and broad way of detecting classification outliers in Bayesian Neural Networks? If a sample is detected as an outlier, is there a way of separating between different types of outliers?
We try to answer these questions by using the pre-activation neuron values of a Bayesian Neural Network. We compare, in total, three different methods using simulated data, the Breast Cancer Wisconsin dataset and the MNIST dataset. The first method uses the well-researched Predictive Entropy, which will act as a baseline method. The second method uses the pre-activation neuron values in the output layer of a Bayesian Neural Network; this is done by comparing the pre-activation neuron value from a given data sample with the pre-activation neuron values from the training data. Lastly, the third method is a combination of the first two methods.
The results show that the performance might depend on the dataset type. The proposed method outperforms the baseline method on the simulated data. When using the Breast Cancer Wisconsin dataset, we see that the proposed method is significantly better than the baseline. Interestingly, we observe that with the MNIST dataset, the baseline model outperforms the proposed method in most scenarios. Common for all three datasets is that the combination of the two methods performs approximately as well as the best of the two
