Few-shot Learning from Meta-Learning, Statistical Understanding to Applications

There is a growing trend of research in few-shot learning (FSL), which involves adapting learned knowledge to learn new concepts with limited few-shot training examples. This tutorial comprises several talks, including an overview of few-shot learning by Dr. Da Li and a discussion of seminal and state-of-the-art meta-learning methods for FSL by Prof. Timothy Hospedales. The tutorial will cover both gradient-based and amortised meta-learners, as well as some theory for meta-learning, and Dr. Yanwei Fu will introduce recent FSL techniques that use statistical methods, such as exploiting the support of unlabeled instances for few-shot visual recognition and causal inference for few-shot learning. Dr. Yu-Xiong Wang will also discuss various applications of FSL in fields beyond computer vision, such as natural language processing, reinforcement learning, and robotics.