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 51–60 of 305 papers

TitleStatusHype
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
Learning from similarity and information extraction from structured documentsCode0
Active Use of Latent Constituency Representation in both Humans and Large Language ModelsCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash FunctionsCode0
Few-Shot Knowledge Graph CompletionCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
Encoding Matching Criteria for Cross-domain Deformable Image RegistrationCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Show:102550
← PrevPage 6 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified