On Hard Episodes in Meta-Learning

Samyadeep Basu; Amr Sharaf; Nicolo Fusi; Soheil Feizi

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arXiv · 2110.11190

On Hard Episodes in Meta-Learning

Samyadeep Basu·Amr Sharaf·Nicolo Fusi·Soheil Feizi

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Abstract

Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a wide gap in the meta-learner's performance across episodes. Understanding this issue is particularly critical in industrial few-shot settings, where there is limited control over test episodes as they are typically uploaded by end-users. In this paper, we empirically analyse the behaviour of meta-learners on episodes of varying hardness across three standard benchmark datasets: CIFAR-FS, mini-ImageNet, and tiered-ImageNet. Surprisingly, we observe a wide gap in accuracy of around 50% between the hardest and easiest episodes across all the standard benchmarks and meta-learners. We additionally investigate various properties of hard episodes and highlight their connection to catastrophic forgetting during meta-training. To address the issue of sub-par performance on hard episodes, we investigate and benchmark different meta-training strategies based on adversarial training and curriculum learning. We find that adversarial training strategies are much more powerful than curriculum learning in improving the prediction performance on hard episodes.

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Samyadeep Basu, Amr Sharaf, Nicolo Fusi, Soheil Feizi. 2021-10-21. On Hard Episodes in Meta-Learning. https://arxiv.org/abs/2110.11190

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