Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings

TL;DR

This paper evaluates the effectiveness of Bayesian deep learning models in out-of-distribution detection and adversarial robustness, revealing marginal improvements and highlighting factors influencing model sensitivity to unseen data.

Contribution

It systematically tests likelihood-based Bayesian models for OoD detection and adversarial robustness, uncovering their limitations and the impact of architectural choices.

Findings

Bayesian models sometimes outperform conventional neural networks in OoD detection.

Models show reduced AUC scores when in/out distributions have minimal overlap.

Initialisation, architecture, and activation functions influence model sensitivity to unseen data.

Abstract

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.