  Previous works on meta-learning either relied on elaborately hand-designed
network structures or adopted specialized learning rules to a particular
domain. We propose a universal framework to optimize the meta-learning process
automatically by adopting neural architecture search technique (NAS). NAS
automatically generates and evaluates meta-learner's architecture for few-shot
learning problems, while the meta-learner uses meta-learning algorithm to
optimize its parameters based on the distribution of learning tasks. Parameter
sharing and experience replay are adopted to accelerate the architectures
searching process, so it takes only 1-2 GPU days to find good architectures.
Extensive experiments on Mini-ImageNet and Omniglot show that our algorithm
excels in few-shot learning tasks. The best architecture found on Mini-ImageNet
achieves competitive results when transferred to Omniglot, which shows the high
transferability of architectures among different computer vision problems.
