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Poster
Fast Context Adaptation via Meta-Learning
Luisa Zintgraf · Kyriacos Shiarlis · Vitaly Kurin · Katja Hofmann · Shimon Whiteson

Wed Jun 12 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #252
in Posters Wed »

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.

Keywords: Architectures · Deep Reinforcement Learning · Representation Learning · Supervised Learning · Transfer and Multitask Learning

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)

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