Meta Training Stage

Meta-training focuses on efficiently and effectively learning to learn, improving the adaptability and performance of machine learning models, particularly in few-shot learning scenarios. Current research emphasizes optimizing task selection during meta-training, incorporating information from base classes or pre-training stages, and developing methods for robust generalization across diverse datasets. These advancements aim to reduce training costs, improve model accuracy, and enhance the explainability and trustworthiness of AI systems, impacting fields ranging from cognitive modeling to medical diagnosis.