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 71–80 of 305 papers

TitleStatusHype
Face Verification and Forgery Detection for Ophthalmic Surgery Images—0
Fast Adaptation in Generative Models with Generative Matching Networks—0
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching—0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models—0
Automatic detection of rare pathologies in fundus photographs using few-shot learning—0
Dynamic Spectrum Matching with One-shot Learning—0
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
Distributional Modeling on a Diet: One-shot Word Learning from Text Only—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified