Automatic Metric Search for Few-Shot Learning.
IEEE Trans Neural Netw Learn Syst
; PP2023 Feb 08.
Article
en En
| MEDLINE
| ID: mdl-37022809
ABSTRACT
Few-shot learning (FSL) aims to learn a model that can identify unseen classes using only a few training samples from each class. Most of the existing FSL methods adopt a manually predefined metric function to measure the relationship between a sample and a class, which usually require tremendous efforts and domain knowledge. In contrast, we propose a novel model called automatic metric search (Auto-MS), in which an Auto-MS space is designed for automatically searching task-specific metric functions. This allows us to further develop a new searching strategy to facilitate automated FSL. More specifically, by incorporating the episode-training mechanism into the bilevel search strategy, the proposed search strategy can effectively optimize the network weights and structural parameters of the few-shot model. Extensive experiments on the miniImageNet and tieredImageNet datasets demonstrate that the proposed Auto-MS achieves superior performance in FSL problems.
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01-internacional
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MEDLINE
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En
Revista:
IEEE Trans Neural Netw Learn Syst
Año:
2023
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Article