Challenging the Fast Adaptation Paradigm: Slow-and-Steady Meta-Learning for Long-Tail Tasks

Most papers—including Zeng et al. and Hiller et al.—assume that rapid adaptation is always desirable. But what if, for highly imbalanced or long-tail task distributions (see Dong et al., Perera & Halgamuge), slower, more deliberate adaptation actually improves reliability and prevents catastrophic forgetting? This idea proposes meta-learning algorithms that control adaptation speed using meta-learned patience parameters or curriculum-based update schedules. By purposely introducing “friction” in gradient updates (perhaps via scheduled regularization or trust-region criteria), the model can avoid overfitting to spurious few-shot samples and better generalize to rare or outlier tasks. This fundamentally questions a central assumption in meta-learning and could open up new avenues for robust adaptation, especially in domains plagued by noisy, imbalanced, or adversarial data.

References:

1. Few-Shot Fast Adaptation Strategies with Meta-Learning and Multi-Armed Bandits. Zhipeng Dong (2025). Applied and Computational Engineering.
2. Few-Shot SAR Target Recognition Through Meta-Adaptive Hyperparameters’ Learning for Fast Adaptation. Zhiqiang Zeng, J. Sun, Yanping Wang, Dandan Gu, Zhu Han, Wen Hong (2023). IEEE Transactions on Geoscience and Remote Sensing.
3. Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning. Rashindrie Perera, S. Halgamuge (2024). Computer Vision and Pattern Recognition.
4. On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation. Markus Hiller, Mehrtash Harandi, T. Drummond (2022). Neural Information Processing Systems.
5. Few-Shot Fast Adaptation Strategies with Meta-Learning and Multi-Armed Bandits. Zhipeng Dong (2025). Applied and Computational Engineering.
6. Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning. Rashindrie Perera, S. Halgamuge (2024). Computer Vision and Pattern Recognition.