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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 305 papers

TitleStatusHype
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification—0
One-shot recognition of any material anywhere using contrastive learning with physics-based renderingCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
AROS: Affordance Recognition with One-Shot Human Stances—0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
BaseTransformers: Attention over base data-points for One Shot LearningCode1
One-shot Detail Retouching with Patch Space Neural Transformation BlendingCode0
One-Shot Learning of Stochastic Differential Equations with Data Adapted Kernels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified