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 211–220 of 305 papers

TitleStatusHype
Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision—0
TET-GAN: Text Effects Transfer via Stylization and Destylization—0
What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detectionCode0
That's Mine! Learning Ownership Relations and Norms for Robots—0
Trading robust representations for sample complexity through self-supervised visual experience—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
One-Shot Instance SegmentationCode1
Tackling Early Sparse Gradients in Softmax Activation Using Leaky Squared Euclidean Distance—0
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identificationCode0
Face Verification and Forgery Detection for Ophthalmic Surgery Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified