Rapid Adaptation with Conditionally Shifted Neurons

TL;DR

This paper introduces conditionally shifted neurons, a mechanism enabling neural networks to rapidly adapt to new tasks with minimal data, achieving state-of-the-art results in vision and language benchmarks.

Contribution

The paper proposes conditionally shifted neurons for fast task adaptation in neural networks within a meta-learning framework, demonstrating superior performance.

Findings

Achieved state-of-the-art results on vision and language benchmarks.

Enabled rapid adaptation with limited task data.

Improved flexibility of neural networks in meta-learning settings.

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.