Context-aware Meta-learning

Fifty Christopher, Duan Dennis, Junkins Ronald G., Amid Ehsan, Leskovec Jure, Re Christopher, Thrun Sebastian. Arxiv 2023

[Paper] [Code]    
Fine Tuning GPT Has Code In Context Learning Model Architecture Pretraining Methods Prompting RAG Reinforcement Learning Training Techniques

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 visual 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. Our code is available at https://github.com/cfifty/CAML.

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