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 41–50 of 305 papers

TitleStatusHype
Active Use of Latent Constituency Representation in both Humans and Large Language ModelsCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
Improving Siamese Networks for One Shot Learning using Kernel Based Activation functionsCode0
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image RecognitionCode0
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract ReasoningCode0
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics SystemCode0
Generalization in Machine Learning via Analytical Learning TheoryCode0
It's DONE: Direct ONE-shot learning with quantile weight imprintingCode0
Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget ExperiencesCode0
Encoding Matching Criteria for Cross-domain Deformable Image RegistrationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified