Out-of-Distribution Sample Detection Technology Based On Sample Enhancement And Gradient Distribution Matching

With the rapid popularization and application of artificial intelligence in many fields,deep learning as a powerful machine learning technology has attracted increasing attention.However,the problems of overfitting and insufficient generalization ability of deep learning models have become bottlenecks that restrict their performance and reliability.Among them,when faced with samples from unknown domains or with different distributions,deep learning models often make misclassifications with high confidence,which requires research and improvement on the model’s out-of-distribution(OOD)detection.This paper aims to explore the OOD detection methods in deep learning.We first introduce the popular phenomena in artificial intelligence and the development history of deep learning technology.Then,we discuss in detail the problems of deep learning models in facing OOD samples,and expound the concept of OOD detection and its importance in deep learning.Next,we outline the existing OOD detection technologies,including post-hoc detection,densitybased,distance-based methods,and point out the shortcomings of existing methods.Subsequently,we propose two novel OOD detection methods.The first method improves the OOD detection effect through sample enhancement,which is versatile and can be applied to various OOD detection methods,and has achieved remarkable performance improvement.The second method is based on the gradient distribution of model predictions.The gradient information is less used in current OOD detection,and this method is a feasible way to utilize gradient distribution information.We also propose some improvement strategies for this method for future research in chapter 5.In summary,the contributions of this paper are as follows:(1)We propose two OOD detection methods based on uncertainty and theoretically illustrate their effectiveness.We also demonstrate their effectiveness through experiments on multiple models and datasets.(2)The OOD detection method based on sample enhancement proposed in this paper can not only be based on baseline methods but also be applicable to various other OOD detection methods,improving their detection performance.(3)We propose a method using gradient space for OOD detection and put forward some conjectures for further research on related issues.