Few-Shot Learning
A machine learning paradigm in which a system learns new tasks from only a small number of examples. It serves as an important measure of AI generalization capability and represents a notable advantage of human intelligence over current AI systems.
Timeline (last 90 days)
Under this framework, few-shot learning corresponds to the constraint harness restricting the use of only a very small number of labeled samples, while the verifier still uses the standard test set.
Traditionally, few-shot learning, domain adaptation, and multimodal fusion in the remote sensing field have been regarded as mutually independent research problems, each with a different methodology.
Models with fewer than 7B parameters almost always must be used together with few-shot examples.
Human learning of new skills relies on prior knowledge transfer, causal reasoning, and highly compressed conceptual representations, a capability referred to in cognitive science as inductive bias.
Runtime policy definition is a technique combining prompt engineering and few-shot learning that allows temporarily describing in natural language, at each inference call, the content to be intercepted or permitted.
All Facts (5)
Runtime policy definition is a technique combining prompt engineering and few-shot learning that allows temporarily describing in natural language, at each inference call, the content to be intercepted or permitted.
60%UnverifiedUnder this framework, few-shot learning corresponds to the constraint harness restricting the use of only a very small number of labeled samples, while the verifier still uses the standard test set.
50%UnverifiedTraditionally, few-shot learning, domain adaptation, and multimodal fusion in the remote sensing field have been regarded as mutually independent research problems, each with a different methodology.
50%UnverifiedModels with fewer than 7B parameters almost always must be used together with few-shot examples.
50%UnverifiedHuman learning of new skills relies on prior knowledge transfer, causal reasoning, and highly compressed conceptual representations, a capability referred to in cognitive science as inductive bias.
50%