Context-Aware Meta-Learning
Authors: name: Christopher Fifty, name: Dennis Duan, name: Ronald G. Junkins, name: Ehsan Amid, name: Jure Leskovec, name: Christopher Ré, name: Sebastian Thrun

Large Language Models like ChatGPT demonstrate a remarkable capacity to learn
new concepts during inference without any fine-tuning. However, visual models
trained to detect new objects during inference have been unable to replicate
this ability, and instead either perform poorly or require meta-training and/or
fine-tuning on similar objects. In this work, we propose a meta-learning
algorithm that emulates Large Language Models by learning new visual concepts
during inference without fine-tuning. Our approach leverages a frozen
pre-trained feature extractor, and analogous to in-context learning, recasts
meta-learning as sequence modeling over datapoints with known labels and a test
datapoint with an unknown label. On 8 out of 11 meta-learning benchmarks, our
approach -- without meta-training or fine-tuning -- exceeds or matches the
state-of-the-art algorithm, P>M>F, which is meta-trained on these benchmarks.