  Model agnostic meta-learning (MAML) is one of the most widely used
gradient-based meta-learning, consisting of two optimization loops: an inner
loop and outer loop. MAML learns the new task from meta-initialization
parameters with an inner update and finds the meta-initialization parameters in
the outer loop. In general, the injection of noise into the gradient of the
model for augmenting the gradient is one of the widely used regularization
methods. In this work, we propose a novel cooperative meta-learning framework
dubbed CML which leverages gradient-level regularization with gradient
augmentation. We inject learnable noise into the gradient of the model for the
model generalization. The key idea of CML is introducing the co-learner which
has no inner update but the outer loop update to augment gradients for finding
better meta-initialization parameters. Since the co-learner does not update in
the inner loop, it can be easily deleted after meta-training. Therefore, CML
infers with only meta-learner without additional cost and performance
degradation. We demonstrate that CML is easily applicable to gradient-based
meta-learning methods and CML leads to increased performance in few-shot
regression, few-shot image classification and few-shot node classification
tasks. Our codes are at https://github.com/JJongyn/CML.
