On Hard Episodes in Meta-Learning
This addresses a critical issue in industrial few-shot learning where test episodes are user-uploaded and uncontrolled, though it is incremental as it builds on existing meta-learning methods.
The paper investigates the performance gap of meta-learners on episodes of varying hardness, finding a 50% accuracy difference between hardest and easiest episodes across standard benchmarks, and shows adversarial training outperforms curriculum learning for improving hard episode performance.
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.