Multi-task convex combination interpolation for meta-learning with fewer tasks

Meta-learning methods try to enhance the generalization of the meta-learning model by various tasks. Diverse tasks can provide sufficient knowledge to assist the model in understanding and capturing different types of information. The sufficient number of meta-training tasks is a crucial factor in ensuring the generalization of meta-learning algorithms. However, obtaining a sufficient number of labeled meta-training tasks is often a challenging endeavor in real-world scenarios. Some scholars have addressed the issue of inadequate tasks only by interpolating between two tasks, but they do not take into account the problem of task diversity. To resolve this concern, we propose a Multi-task Convex Combination Interpolation (MCCI) method that simultaneously considers both task quantity and task diversity issues. First, we increase the number of tasks based on interpolation techniques, which provides the possibility to extend it from 2-task interpolation to n-task interpolation. Second, we randomly select multiple tasks and perform convex combination interpolation in the n-task space, in order to increase the task diversity. Third, we prove the positive effect of the convex combination interpolation on the generalization ability and noise resistance of meta-learning algorithms in theory. Finally, we verify the generalization ability and noise resistance of the proposed MCCI on seven datasets. The extensive experimental results show the superior performance of the proposed method compared to the state-of-art methods.