Meta-learning, or "learning to learn", enables machines to acquire general priors with minimal supervision and rapidly adapt to new tasks. Unlike traditional AI methods that approach each task from scratch using a fixed learning algorithm, meta-learning refines the learning algorithm itself through experience across various tasks, enhancing transferability and generalization. This is especially valuable when data collection is difficult or costly, allowing for effective learning from task sequences while reducing the dependency on extensive target domain data. Consequently, meta-learning has emerged as a promising field in machine learning. Although existing surveys provide valuable insights into meta-learning, they often present methods and applications in isolation and lack coverage of the latest advancements. Given the rapid growth of the field, a comprehensive survey is both necessary and challenging. Moreover, meta-learning algorithms often remain disconnected, with no unified framework to explain how they facilitate "learning to learn". This survey seeks to bridge that gap by systematizing meta-learning research, offering a thorough overview of strategies to enhance understanding. Additionally, the paper reviews over thirty representative meta-learning methods across models, tasks, and applications, analyzing their characteristics and challenges. To illustrate method performance, we evaluate more than fifteen models on six problems spanning thirteen scenarios, emphasizing the importance of selecting appropriate meta-learning approaches for practical applications.

Artificial intelligence technology has already had a profound impact in various fields such as economy, industry, and education, but still limited. Meta-learning, also known as “learning to learn”, provides an opportunity for general artificial intelligence, which can break through the current AI bottleneck. However, meta learning started late and there are fewer projects compare with CV, NLP etc. Each deployment requires a lot of experience to configure the environment, debug code or even rewrite, and the frameworks are isolated. Moreover, there are currently few platforms that focus exclusively on meta-learning, or provide learning materials for novices, for which the threshold is relatively high. Based on this, Awesome-META+, a meta-learning framework integration and learning platform is proposed to solve the above problems and provide a complete and reliable meta-learning framework application and learning platform. The project aims to promote the development of meta-learning and the expansion of the community, including but not limited to the following functions: 1) Complete and reliable meta-learning framework, which can adapt to multi-field tasks such as target detection, image classification, and reinforcement learning. 2) Convenient and simple model deployment scheme which provide convenient meta-learning transfer methods and usage methods to lower the threshold of meta-learning and improve efficiency. 3) Comprehensive researches for learning. 4) Objective and credible performance analysis and thinking.