AwesomeMeta+: Bridging the Technical Barriers to Meta-Learning via A Prototyping and Learning System

Meta-learning, also known as "learning to learn", enables models to acquire great generalization abilities by learning from various tasks. Recent advancements have made these models applicable across various fields without data constraints, offering new opportunities for general artificial intelligence. However, applying these models can be challenging due to their often task-specific, standalone nature and the technical barriers involved. To address this challenge, we develop AwesomeMeta+, a prototyping and learning system that standardizes different components of meta-learning and uses a building block metaphor to assist in model construction. AwesomeMeta+ allows users to assemble compatible algorithm modules to meet the application needs in practice. To optimize AwesomeMeta+, we provide the interface to 50 researchers and refine the design based on their feedback. Through machine-based testing and user studies, we demonstrate that AwesomeMeta+ enhances users' understanding of the related technologies and accelerates their engineering processes by offering guidance for meta-learning deployments.