mlsanityData to 7 Oct 2026

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

arXiv 1703.03400 ↗First posted 9 Mar 2017cs.LG cs.AI cs.CVcited by 4,861

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Cited by 99 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

Key facts. Cited by 4,861 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2017.

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Citing papers per year: 2018: 7; 2019: 7; 2020: 8; 2021: 1008; 2022: 965; 2023: 941; 2024: 758; 2025: 672; 2026: 495.

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  13. 2024Automated Design of Agentic Systemscited by 360
  14. 2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
  15. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
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