Task augmentation via channel mixture for few-task meta-learning

Abstract

Meta-learning is a promising approach for rapidly adapting to new tasks with minimal data by leveraging knowledge from previous tasks. However, meta-learning typically requires a large number of meta-training tasks. Existing methods often generate new tasks by interpolating fine-grained feature points, and such interpolation can compromise the continuity and integrity of the feature representations in the generated tasks. To address this problem, we propose task-level data augmentation to generate additional new tasks. Specifically, we introduce a novel task augmentation method called Task Augmentation via Channel Mixture (TACM). TACM generates new tasks by mixing channels from different tasks. This channel-level mixture preserves the continuity and integrity of feature information in channels during the mixture process, thereby enhancing the generalization ability of the model. Experimental results demonstrate that TACM outperforms other state-of-the-art methods across multiple datasets. Code is available at https://github.com/F-GOD6/TACM.