Meta-Learning Strategies for Efficient Knowledge Transfer in Multitask Machine Learning Environments

Abstract

Multitask learning aims to enhance the performance of machine learning models by simultaneously training on multiple related tasks. However, in complex and dynamic environments, transferring knowledge across tasks efficiently remains a challenging endeavor. This research proposes a meta-learning framework designed to optimize knowledge transfer in multitask machine learning scenarios. The proposed strategies leverage meta-learning techniques to enable models to quickly adapt to new tasks by leveraging knowledge acquired from previous tasks. The framework encompasses three key components: a meta-learning algorithm, a task embedding mechanism, and a knowledge transfer module. The meta-learning algorithm facilitates the acquisition of meta-knowledge, enabling the model to learn how to learn across tasks effectively. The task embedding mechanism captures the inherent relationships between tasks, facilitating the extraction of task-specific features and promoting cross-task generalization. The knowledge transfer module leverages the acquired meta-knowledge and task embeddings to guide the transfer of relevant information from source tasks to target tasks.