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 \emph{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.

翻译：摘要：小样本元学习因每项任务训练样本数量有限，给梯度下降优化带来了挑战。为解决此问题，我们提出了一种元学习的情景记忆优化方法，命名为\emph{EMO}，其灵感源自人类从大脑记忆中回忆过往学习经验的能力。EMO将过去经历任务的梯度历史存储于外部记忆中，从而以记忆增强的方式实现小样本学习。通过学习保留并回忆过往训练任务的学习过程，EMO能在有限样本提供的梯度信息不足时，引导参数更新朝向正确方向。我们从理论上证明了该算法在光滑且强凸的目标函数下具有收敛性。EMO具备通用性、灵活性和模型无关性，是一种简单的即插即用优化器，可无缝嵌入现有基于优化的元学习方法中。实验结果表明，EMO在大多数小样本分类基准测试中表现良好，能提升基于优化的元学习方法性能，并加速收敛过程。