Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

Understand

Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks.

• While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing.
• Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on.
• Additionally, a task-specific learner of an {\em MT-net} performs gradient descent with respect to a meta-learned distance metric, which warps the activation space to be more sensitive to task identity.