  Deep neural networks have been successful in diverse discriminative
classification tasks, although, they are poorly calibrated often assigning high
probability to misclassified predictions. Potential consequences could lead to
trustworthiness and accountability of the models when deployed in real
applications, where predictions are evaluated based on their confidence scores.
Existing solutions suggest the benefits attained by combining deep neural
networks and Bayesian inference to quantify uncertainty over the models'
predictions for ambiguous datapoints. In this work we propose to validate and
test the efficacy of likelihood based models in the task of out of distribution
detection (OoD). Across different datasets and metrics we show that Bayesian
deep learning models on certain occasions marginally outperform conventional
neural networks and in the event of minimal overlap between in/out distribution
classes, even the best models exhibit a reduction in AUC scores in detecting
OoD data. Preliminary investigations indicate the potential inherent role of
bias due to choices of initialisation, architecture or activation functions. We
hypothesise that the sensitivity of neural networks to unseen inputs could be a
multi-factor phenomenon arising from the different architectural design choices
often amplified by the curse of dimensionality. Furthermore, we perform a study
to find the effect of the adversarial noise resistance methods on in and
out-of-distribution performance, as well as, also investigate adversarial noise
robustness of Bayesian deep learners.
