SOTAVerified

One-Shot Learning

One-shot learning is the task of learning information about object categories from a single training example.

( Image credit: Siamese Neural Networks for One-shot Image Recognition )

Papers

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Showing 261–270 of 305 papers

TitleStatusHype
BAE-NET: Branched Autoencoder for Shape Co-SegmentationCode0
One-shot Joint Extraction, Registration and Segmentation of Neuroimaging DataCode0
Encoding Matching Criteria for Cross-domain Deformable Image RegistrationCode0
One-shot Learning with Memory-Augmented Neural NetworksCode0
Siamese neural networks for one-shot image recognitionCode0
Learning from similarity and information extraction from structured documentsCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Attentive Recurrent ComparatorsCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
A Deep One-Shot Network for Query-based Logo RetrievalCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified