  Meta-learning aims at learning quickly on novel tasks with limited data by
transferring generic experience learned from previous tasks. Naturally,
few-shot learning has been one of the most popular applications for
meta-learning. However, existing meta-learning algorithms rarely consider the
time and resource efficiency or the generalization capacity for unknown
datasets, which limits their applicability in real-world scenarios. In this
paper, we propose MetaDelta, a novel practical meta-learning system for the
few-shot image classification. MetaDelta consists of two core components: i)
multiple meta-learners supervised by a central controller to ensure efficiency,
and ii) a meta-ensemble module in charge of integrated inference and better
generalization. In particular, each meta-learner in MetaDelta is composed of a
unique pretrained encoder fine-tuned by batch training and parameter-free
decoder used for prediction. MetaDelta ranks first in the final phase in the
AAAI 2021 MetaDL
Challenge\footnote{https://competitions.codalab.org/competitions/26638},
demonstrating the advantages of our proposed system. The codes are publicly
available at https://github.com/Frozenmad/MetaDelta.
