Title: Rapid Adaptation with Conditionally Shifted Neurons,
Abstract: We describe a mechanism by which artificial neural networks can learn rapid
adaptation - the ability to adapt on the fly, with little data, to new tasks -
that we call conditionally shifted neurons. We apply this mechanism in the
framework of metalearning, where the aim is to replicate some of the
flexibility of human learning in machines. Conditionally shifted neurons modify
their activation values with task-specific shifts retrieved from a memory
module, which is populated rapidly based on limited task experience. On
metalearning benchmarks from the vision and language domains, models augmented
with conditionally shifted neurons achieve state-of-the-art results.