CAML: Fast Context Adaptation via Meta-Learning

We propose CAML, a meta-learning method for fast adaptation that 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), our method can be scaled up to larger networks without overfitting on a single task, is easier to implement, and saves memory writes during training and network communication at test time for distributed machine learning systems. We show empirically that this approach outperforms MAML, is less sensitive to the task-specific learning rate, can capture meaningful task embeddings with the context parameters, and outperforms alternative partitionings of the parameter vectors. A key challenge in meta-learning is fast adaptation: learning on previously unseen tasks fast and with little data. In principle, this can be achieved by leveraging knowledge obtained in other, related tasks. However, the best way to do so remains an open question. A popular recent method for fast adaptation is model agnostic meta learning (MAML) (Finn et al., 2017a), which learns a model initialisation, such that at test time the model can be adapted to solve the new task in only a few gradient steps. MAML has an interleaved training procedure, comprised of inner loop and outer loop updates that operate on a batch of tasks at each iteration. In the inner loop, MAML learns task-specific parameters by performing one gradient step on a task-specific loss.