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Vinyals et al.: support-conditioned matching networks

Contribution grade

Few-shot learning architectures · Human-designed research method or resource

Reference score140,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Mapped labeled support examples and a query through learned representations and attention to predict unseen-class labels, training on episodes that mimic the one-shot test setting.

Scientific scope

Mapped labeled support examples and a query through learned representations and attention to predict unseen-class labels, training on episodes that mimic the one-shot test setting. Metric learning, attention and external memories are inherited. No-finetuning refers to adaptation by support conditioning, not a network trained without data or universal one-shot learning.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Metric learning, attention and external memories are inherited. No-finetuning refers to adaptation by support conditioning, not a network trained without data or universal one-shot learning.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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