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 171–180 of 305 papers

TitleStatusHype
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification—0
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing—0
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing—0
High-dimensional separability for one- and few-shot learning—0
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis—0
Improving One-Shot Learning through Fusing Side Information—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Interactive Instance Annotation with Siamese Networks—0
Inverting The Generator Of A Generative Adversarial Network—0
Investigation of using disentangled and interpretable representations with language conditioning for cross-lingual voice conversion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified