Context-Aware Meta-Learning
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.
652f4c88262ab88f123e6757
Christopher Fifty
claimed_verified
2023-10-19T08:10:23.862Z
65304af3d107f378e1f5a869
/avatars/fc222f5aee84d09df7906a15388cfc49.svg
Chris Fifty
user
cfifty
652f4c88262ab88f123e6758
Dennis Duan
admin_assigned
2023-10-18T08:36:12.234Z
6445ddc07a7b94ddc2d6e73a
/avatars/a293e9396fec0bd34da44e3816ae6d5c.svg
Dennis Duan
user
dduan97
652f4c88262ab88f123e6759
Ronald G. Junkins
652f4c88262ab88f123e675a
Ehsan Amid
claimed_verified
2023-10-19T08:10:26.656Z
6530385d800ac851b9b06bac
/avatars/b93a59e821deca7c7ca63989f15e98ac.svg
Ehsan Amid
user
ehsanamid
652f4c88262ab88f123e675b
Jure Leskovec
652f4c88262ab88f123e675c
Christopher Ré
652f4c88262ab88f123e675d
Sebastian Thrun
Context-Aware Meta-Learning
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.
A meta-learning algorithm learns new visual concepts during inference without fine-tuning, leveraging a pre-trained feature extractor and sequence modeling.
meta-learning Large Language Models pre-trained feature extractor in-context learning sequence modeling meta-training fine-tuning meta-learning benchmarks