  Few-shot meta-learning presents a challenge for gradient descent optimization
due to the limited number of training samples per task. To address this issue,
we propose an episodic memory optimization for meta-learning, we call EMO,
which is inspired by the human ability to recall past learning experiences from
the brain's memory. EMO retains the gradient history of past experienced tasks
in external memory, enabling few-shot learning in a memory-augmented way. By
learning to retain and recall the learning process of past training tasks, EMO
nudges parameter updates in the right direction, even when the gradients
provided by a limited number of examples are uninformative. We prove
theoretically that our algorithm converges for smooth, strongly convex
objectives. EMO is generic, flexible, and model-agnostic, making it a simple
plug-and-play optimizer that can be seamlessly embedded into existing
optimization-based few-shot meta-learning approaches. Empirical results show
that EMO scales well with most few-shot classification benchmarks and improves
the performance of optimization-based meta-learning methods, resulting in
accelerated convergence.
