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We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.
Author Information
Luisa Zintgraf (University of Oxford)
Kyriacos Shiarlis (University of Amsterdam)
Vitaly Kurin (University of Oxford)
Katja Hofmann (Microsoft)
Shimon Whiteson (University of Oxford)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Fast Context Adaptation via Meta-Learning »
Wed. Jun 12th 09:25 -- 09:30 PM Room Room 201
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