Paper Title
INTEGRATING MULTIPLE UDA METHODS FOR EFFECTIVE TRANSFER LEARNING IN RAD SCENE SEMANTIC SEGMENTATION

Abstract
Abstract - This paper presents a transfer learning-based approach to address challenges in deep learning, such as the cost of labeling and performance loss due to domain differences. The proposed architecture, incorporating multiple UDA methods, demonstrates promising results in various experiments, highlighting the potential for practical applications. Keywords - UDA, Style Transfer, Road Scene Semantic Segmentation.