Through Graphical Models to Address out-of-Distribution Ways

GCTB-Northeastern State University (Tahlequah), USA Joint Institute of Technology, Guangzhou college of technology and business, Foshan, Guangdong province, China

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Abstract

Out-of-distribution (OOD) generalization in deep learning models remains one of the significant challenges in artificial intelligence research. This article will systematically discuss the issue of OOD generalization, including the current primary solutions, comparative analysis of various methods and the future development directions in this field. The article first introduces the issues related to OOD generalization in the autonomous driving field, and then categorizes the mainstream methods for enhancing the OOD generalization ability of models nowadays. The system introduces methods for learning from data without labels, a structure-aware method and uncertainty-aware graph structure learning (UnGSL). Then these methods will be compared and summarized, and their respective advantages and disadvantages will be analyzed and contrasted. Finally, future research directions and plans for enhancing the OOD generalization ability of the model are also proposed. The article aims to provide a clearer and more comprehensive understanding of some breakthrough methods in current research on OOD generalization ability, and promote the application of these methods in actual fields.