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Robust Meta Learning for Image based tasks

arXiv 2301.12698 ↗First posted 30 Jan 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 39 references that are in the map. First posted on arXiv 30 Jan 2023.

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31 of 39 references.

References · 31

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19 more
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