Exploring Foundational Models in Artificial Intelligence with Focus on Generalization Challenges and Domain Adaptation Strategies Across Diverse Applications

Saurabh Verma

Authors

Saurabh Verma USA Author

Keywords:

Foundational Models, Generalization, Domain Adaptation, Artificial Intelligence, Cross-Domain Applications, Deep Learning

Abstract

The emergence of foundational models in artificial intelligence (AI) has revolutionized diverse domains, offering unprecedented capabilities in generalization and cross-domain adaptability. Despite these advancements, significant challenges persist in achieving robust generalization and efficient domain adaptation. This paper provides a concise yet comprehensive analysis of these challenges and explores strategies employed across diverse applications, including natural language processing, computer vision, and robotics. Through a synthesis of literature, we identify key insights into architectural innovations, training methodologies, and domain adaptation techniques. We include empirical data, tables, and graphical analyses to elucidate the findings and suggest potential research avenues to address unresolved issues.

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