Generative Artificial Intelligence
Generative Artificial Intelligence (GenAI) focuses on creating new data samples—text, images, code, etc.—from existing datasets using deep learning models. Current research emphasizes diverse applications, including drug discovery, education, and industrial processes, with a focus on model architectures like transformers, diffusion models, and generative adversarial networks (GANs). The field's significance lies in its potential to automate complex tasks, accelerate scientific discovery, and reshape various industries, while also raising important ethical considerations regarding bias, data privacy, and the responsible use of AI.
Papers
Generative AI for Cel-Animation: A Survey
Yunlong Tang, Junjia Guo, Pinxin Liu, Zhiyuan Wang, Hang Hua, Jia-Xing Zhong, Yunzhong Xiao, Chao Huang, Luchuan Song, Susan Liang, Yizhi Song, Liu He, Jing Bi, Mingqian Feng, Xinyang Li, Zeliang Zhang, Chenliang Xu
Video Summarisation with Incident and Context Information using Generative AI
Ulindu De Silva, Leon Fernando, Kalinga Bandara, Rashmika Nawaratne
Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding
John C. Rollman (1), Bruce Rogers (1), Hamed Zaribafzadeh (1), Daniel Buckland (2), Ursula Rogers (1), Jennifer Gagnon (1), Ozanan Meireles (1), Lindsay Jennings (3), Jim Bennett (1), Jennifer Nicholson (3), Nandan Lad (4), Linda Cendales (1), Andreas Seas (4, 5, 6), Alessandro Martinino (6), E. Shelley Hwang (1), Allan D. Kirk (1)
Physics-Constrained Generative Artificial Intelligence for Rapid Takeoff Trajectory Design
Samuel Sisk, Xiaosong Du
Generative Semantic Communication: Architectures, Technologies, and Applications
Jinke Ren, Yaping Sun, Hongyang Du, Weiwen Yuan, Chongjie Wang, Xianda Wang, Yingbin Zhou, Ziwei Zhu, Fangxin Wang, Shuguang Cui
Competition and Diversity in Generative AI
Manish Raghavan
Watermarking Training Data of Music Generation Models
Pascal Epple, Igor Shilov, Bozhidar Stevanovski, Yves-Alexandre de Montjoye