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 91–100 of 305 papers

TitleStatusHype
Task Adaptive Feature Transformation for One-Shot Learning—0
VGTS: Visually Guided Text Spotting for Novel Categories in Historical Manuscripts—0
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics SystemCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Tab2KG: Semantic Table Interpretation with Lightweight Semantic ProfilesCode0
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy—0
One-shot skill assessment in high-stakes domains with limited data via meta learningCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified