  A machine learning model that generalizes well should obtain low errors on
unseen test examples. Thus, if we learn an optimal model in training data, it
could have better generalization performance in testing tasks. However,
learning such a model is not possible in standard machine learning frameworks
as the distribution of the test data is unknown. To tackle this challenge, we
propose a novel robust meta-learning method, which is more robust to the
image-based testing tasks which is unknown and has distribution shifts with
training tasks. Our robust meta-learning method can provide robust optimal
models even when data from each distribution are scarce. In experiments, we
demonstrate that our algorithm not only has better generalization performance
but also robust to different unknown testing tasks.
