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A machine learning model that generalizes well should obtain low errors onunseen test examples. Thus, if we learn an optimal model in training data, itcould have better generalization performance in testing tasks. However,learning such a model is not possible in standard machine learning frameworksas the distribution of the test data is unknown. To tackle this challenge, wepropose a novel robust meta-learning method, which is more robust to theimage-based testing tasks which is unknown and has distribution shifts withtraining tasks. Our robust meta-learning method can provide robust optimalmodels even when data from each distribution are scarce. In experiments, wedemonstrate that our algorithm not only has better generalization performancebut also robust to different unknown testing tasks.

Robust Meta Learning for Image based tasks