Meta Learning Framework

Meta-learning, or "learning to learn," focuses on developing algorithms that can rapidly adapt to new tasks with limited data, improving generalization and efficiency compared to traditional machine learning. Current research emphasizes the development of meta-learning frameworks tailored to specific applications, such as few-shot learning, continual learning, and federated learning, often employing model architectures like transformers and prototypical networks, along with algorithms like MAML and meta-SGD. This field is significant because it addresses the limitations of traditional methods in data-scarce or rapidly changing environments, impacting diverse areas including autonomous systems, software engineering, and anomaly detection.