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.

翻译：在未知测试样本上表现良好的机器学习模型应获得较低的错误率。因此，若能在训练数据中学习到最优模型，则该模型在测试任务中可能具备更优的泛化性能。然而，由于测试数据分布未知，标准机器学习框架无法实现这一目标。为应对这一挑战，我们提出了一种新型鲁棒元学习方法，该方法对未知的、且与训练任务存在分布偏移的图像测试任务具有更强的鲁棒性。即使各分布下的数据量稀疏，我们的鲁棒元学习方法仍能提供鲁棒的最优模型。实验表明，该算法不仅具有更优的泛化性能，对不同未知测试任务也表现出鲁棒性。