This article discusses the emerging field of few-shot learning, which addresses the challenge of limited labeled data in machine learning. It explores the potential…
Browsing: Few-Shot Learning
This article discusses the advancements in few-/zero-shot learning, which allows machines to learn from a few or even zero labeled samples. It highlights the…
This article introduces a learning framework for underwater acoustic target recognition model with few samples. A semi-supervised fine-tuning method is proposed to improve the…
A research team led by Professor Sanghyun Park from the Department of Robotics and Mechanical Engineering at DGIST has developed a few-shot learning model…
Researchers from the University of Texas, the University of Massachusetts Amherst, and the University of Texas Health Science Center have proposed that Large Language…