Machine learningMachine learning

Online Few-shot Learning

Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.

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Sources

  1. Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930. link
  2. Javed, K., & White, M. (2019). Meta-Learning Representations for Continual Learning. Advances in Neural Information Processing Systems (NeurIPS), 32. link

Related methods

ScholarGateOnline Few-shot Learning (Online Few-shot Learning (Streaming Meta-Learning from Scarce Labels)). Retrieved 2026-06-04 from https://scholargate.app/tr/machine-learning/online-few-shot-learning