Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
Keywords:Machine Learning (ML)
AbstractWe explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how previous attempts to leverage the randomized embeddings induced by the intermediate layers of a dropout BNN can fail due to the distance metric used. We introduce an alternative approach to measuring embedding uncertainty, and demonstrate how incorporating embedding uncertainty improves OOD data identification across three tasks: image classification, language classification, and malware detection.
How to Cite
Nguyen, A. T., Lu, F., Munoz, G. . L., Raff, E., Nicholas, C., & Holt, J. (2022). Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7877-7885. https://doi.org/10.1609/aaai.v36i7.20757
AAAI Technical Track on Machine Learning II