With the growing attention on learning-to-learn new tasks using only a few
examples, meta-learning has been widely used in numerous problems such as
few-shot classification, reinforcement learning, and domain generalization.
However, meta-learning models are prone to overfitting when there are no
sufficient training tasks for the meta-learners to generalize. Although
existing approaches such as Dropout are widely used to address the overfitting
problem, these methods are typically designed for regularizing models of a
single task in supervised training. In this paper, we introduce a simple yet
effective method to alleviate the risk of overfitting for gradient-based
meta-learning. Specifically, during the gradient-based adaptation stage, we
randomly drop the gradient in the inner-loop optimization of each parameter in
deep neural networks, such that the augmented gradients improve generalization
to new tasks. We present a general form of the proposed gradient dropout
regularization and show that this term can be sampled from either the Bernoulli
or Gaussian distribution. To validate the proposed method, we conduct extensive
experiments and analysis on numerous computer vision tasks, demonstrating that
the gradient dropout regularization mitigates the overfitting problem and
improves the performance upon various gradient-based meta-learning frameworks.

本文提出了一种简单而有效的方法，通过在每个参数的内部循环优化中随机删除梯度来缓解基于梯度的元学习的过拟合风险，从而改善了深度神经网络在新任务上的泛化性能。 作者在大量计算机视觉任务上进行了实验和分析，证明了梯度丢失规范化可以缓解过拟合问题并提高各种基于梯度的元学习框架的性能。