Data Augmentation for Meta-Learning
This work addresses the challenge of data scarcity in meta-learning for few-shot classification, offering a domain-specific solution.
The paper tackled the problem of data augmentation in meta-learning by systematically integrating augmentation at image and class levels, resulting in significant performance improvements on few-shot classification benchmarks.
Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms sample support data, query data, and tasks on each training step. In this complex sampling scenario, data augmentation can be used not only to expand the number of images available per class, but also to generate entirely new classes/tasks. We systematically dissect the meta-learning pipeline and investigate the distinct ways in which data augmentation can be integrated at both the image and class levels. Our proposed meta-specific data augmentation significantly improves the performance of meta-learners on few-shot classification benchmarks.