Out-of-distribution Data Detection using Bayesian Convolutional Neural Network with Variational Inference

Koki Minagawa, Taisei Saito, Sena Kojima, Kota Ando, Tetsuya Asai

Abstract: Out-of-distribution (OOD) data detection is critical when using machine learning models for practical applications. Many methods have been proposed to estimate the uncertainty of machine learning models for input data. Therefore, the purpose of this paper is to propose and verify toward elucidation of the nature and the practical use. We proposes data preprocessing to improve OOD image data detection performance using Bayesian neural networks (BNNs) to estimate two types of uncertainties. The aim of the proposed preprocessing is to increase the usefulness of the two uncertainties by providing more information to the BNN. The proposed preprocessing extracts high-frequency components of the input image by wavelet transform and adds the channel direction to create new input data. The contributions of this paper are as follows. First, we show that in training FashionMNIST, the BNN has a high performance in detecting OOD data by epistemic uncertainty that can be estimated. Second, we infer the relationship between the degree of BNN learning and OOD data detection performance. Finally, the proposed pre-processing is demonstrated to improve the OOD data detection performance. The validation results show that the average area under the receiver operating characteristic (AUROC) increases from 0.936 to 0.970 with the proposed preprocessing for BNNs trained in 10 epochs. These contributions reveal some aspects of the nature of uncertainty and provide clues to approaches for practical applications.