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

Wed Jun 12 02:25 PM -- 02:30 PM (PDT) @ Room 201
in Transfer and Multitask Learning »

We propose CAVIA, a meta-learning method for fast adaptation that is scalable, flexible, and easy to implement. 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, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network. Compared to approaches that adjust all parameters on a new task (e.g., MAML), CAVIA can be scaled up to larger networks without overfitting on a single task, is easier to implement, and is more robust to the inner-loop learning rate. We show empirically that CAVIA outperforms MAML on regression, classification, and reinforcement learning problems.

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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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