  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.
