Performance evaluation of Bayesian neural networks in detecting out-of-distribution image data and a study on data preprocessing

抄録

Out-of-distribution (OOD) data detection is a key challenge in securing artificial intelligence (AI) applications. To address this challenge, this study focused on Bayesian neural networks (BNNs), which can estimate uncertainty in AI. This study performed two major verifications to validate the effectiveness of BNNs in OOD detection One was the verification of the basic OOD detection performance of BNN, and the other was that of the effectiveness of the image data preprocessing method proposed herein to improve the performance. The results showed that all BNNs trained on five benchmark data sets exhibited high OOD data detection performance. Further, the conditions under which BNNs can achieve higher performance were identified. Subsequently, the proposed method was shown to increase the OOD data detection performance on certain data.