Adaptation Concern
Adaptation concern in machine learning focuses on efficiently tailoring large pre-trained models to specific tasks or domains without retraining the entire model. Current research heavily emphasizes low-rank adaptation (LoRA) techniques and their variants, often applied to transformer-based models like LLMs and diffusion models, to achieve parameter efficiency and improved performance. This research area is significant because it addresses the computational cost and memory limitations associated with fine-tuning massive models, enabling broader application and deployment of advanced AI systems across diverse tasks and resource-constrained environments. Furthermore, investigations into bias mitigation and improved adaptation strategies within these frameworks are actively pursued.
Papers
Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation
Xinyi Wang, Sebastian Ruder, Graham Neubig
One-Shot Adaptation of GAN in Just One CLIP
Gihyun Kwon, Jong Chul Ye
AI Autonomy : Self-Initiated Open-World Continual Learning and Adaptation
Bing Liu, Sahisnu Mazumder, Eric Robertson, Scott Grigsby
VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptation
Jiong Wang, Zhou Zhao, Weike Jin, Xinyu Duan, Zhen Lei, Baoxing Huai, Yiling Wu, Xiaofei He
Towards technological adaptation of advanced farming through AI, IoT, and Robotics: A Comprehensive overview
Md. Mahadi Hasan, Muhammad Usama Islam, Muhammad Jafar Sadeq