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 276–300 of 305 papers

TitleStatusHype
Comparison of Maximum Likelihood and GAN-based training of Real NVPs—0
Distributional Modeling on a Diet: One-shot Word Learning from Text Only—0
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion—0
Generative Adversarial Residual Pairwise Networks for One Shot Learning—0
Learning to Remember Rare EventsCode0
Attentive Recurrent ComparatorsCode0
Active One-shot LearningCode0
Discovering objects and their relations from entangled scene representations—0
Fast Adaptation in Generative Models with Generative Matching Networks—0
Inverting The Generator Of A Generative Adversarial Network—0
Low Data Drug Discovery with One-shot Learning—0
Latent Attention For If-Then Program Synthesis—0
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration—0
Task Specific Adversarial Cost Function—0
Learning to learn with backpropagation of Hebbian plasticityCode0
Towards Generalizable Sentence Embeddings—0
Learning feed-forward one-shot learners—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
One-Shot Learning of Scene Locations via Feature Trajectory Transfer—0
One-shot Learning with Memory-Augmented Neural NetworksCode0
Modeling Time Series Similarity with Siamese Recurrent Networks—0
One Shot Learning via Compositions of Meaningful Patches—0
One-Shot Learning of Manipulation Skills with Online Dynamics Adaptation and Neural Network Priors—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
Siamese neural networks for one-shot image recognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified