An Efficient Data Augmentation Network for Out-of-Distribution Image Detection

Since deep neural networks may classify out-of-distribution image data into in-distribution classes with high confidence scores, this problem may cause serious or even fatal hazards in certain applications, such as autonomous vehicles and medical diagnosis. Therefore, out-of-distribution detection (also called anomaly detection or outlier detection) of image classification has become a critical issue for the successful development of neural networks. In other words, a successful neural network needs to be able to distinguish anomalous data that is significantly different from the data used in training. In this paper, we propose an efficient data augmentation network to detect out-of-distribution image data by introducing a set of common geometric operations into training and testing images. The output predicted probabilities of the augmented data are combined by an aggregation function to provide a confidence score to distinguish between in-distribution and out-of-distribution image data. Different from other approaches that use out-of-distribution image data for training networks, we only use in-distribution image data in the proposed data augmentation network. This advantage makes our approach more practical than other approaches, and can be easily applied to various neural networks to improve security in practical applications. The experimental results show that the proposed data augmentation network outperforms the state-of-the-art approaches in various datasets. In addition, pre-training techniques can be integrated into the data augmentation network to make substantial improvements to large and complex data sets. The code is available at https://www.github.com/majic0626/Data-Augmentation-Network.git.