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 11–20 of 305 papers

TitleStatusHype
CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and GeneralizationCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
BaseTransformers: Attention over base data-points for One Shot LearningCode1
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel NoisesCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
Detecting Hate Speech with GPT-3Code1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Echo-SyncNet: Self-supervised Cardiac View Synchronization in EchocardiographyCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Show:102550
← PrevPage 2 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified