Data Augmentation for Meta-Learning
- URL: http://arxiv.org/abs/2010.07092v2
- Date: Tue, 22 Jun 2021 16:06:36 GMT
- Title: Data Augmentation for Meta-Learning
- Authors: Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
- Abstract summary: meta-learning algorithms sample data, query data, and tasks on each training step.
Data augmentation can be used not only to expand the number of images available per class, but also to generate entirely new classes/tasks.
Our proposed meta-specific data augmentation significantly improves the performance of meta-learners on few-shot classification benchmarks.
- Score: 58.47185740820304
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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