Dynamic evolution of competing same-dip double subduction: New perspectives of the Neo-Tethyan plate tectonics

Roy, Arnab; Mandal, Nibir; Van Hunen, Jeroen

Abstract

Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain.

Citation

Roy, A., Mandal, N., & Van Hunen, J. (2024). Dynamic evolution of competing same-dip double subduction: New perspectives of the Neo-Tethyan plate tectonics. Earth and Planetary Science Letters, 647, Article 119032. https://doi.org/10.1016/j.epsl.2024.119032

• Journal Article Type: Article
• Acceptance Date: Sep 24, 2024
• Online Publication Date: Dec 1, 2024
• Publication Date: Dec 1, 2024
• Deposit Date: Dec 2, 2024
• Publicly Available Date: Dec 2, 2024
• Journal: Earth and Planetary Science Letters
• Print ISSN: 0012-821X
• Electronic ISSN: 1385-013X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 647
• Article Number: 119032
• DOI: https://doi.org/10.1016/j.epsl.2024.119032
• Keywords: Artificial intelligence, machine learning, feature extraction, geometrical analysis, content synthesis.
• Public URL: https://durham-repository.worktribe.com/output/3197430