  Model-Agnostic Meta-Learning (MAML) is one of the most successful
meta-learning techniques for few-shot learning. It uses gradient descent to
learn commonalities between various tasks, enabling the model to learn the
meta-initialization of its own parameters to quickly adapt to new tasks using a
small amount of labeled training data. A key challenge to few-shot learning is
task uncertainty. Although a strong prior can be obtained from meta-learning
with a large number of tasks, a precision model of the new task cannot be
guaranteed because the volume of the training dataset is normally too small. In
this study, first,in the process of choosing initialization parameters, the new
method is proposed for task-specific learner adaptively learn to select
initialization parameters that minimize the loss of new tasks. Then, we propose
two improved methods for the meta-loss part: Method 1 generates weights by
comparing meta-loss differences to improve the accuracy when there are few
classes, and Method 2 introduces the homoscedastic uncertainty of each task to
weigh multiple losses based on the original gradient descent,as a way to
enhance the generalization ability to novel classes while ensuring accuracy
improvement. Compared with previous gradient-based meta-learning methods, our
model achieves better performance in regression tasks and few-shot
classification and improves the robustness of the model to the learning rate
and query sets in the meta-test set.
