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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

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.

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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