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 241–250 of 305 papers

TitleStatusHype
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary VideoCode0
One-shot learning of paired association navigation with biologically plausible schemasCode0
Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot ClassificationCode0
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image RecognitionCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Abstracted Gaussian Prototypes for One-Shot Concept LearningCode0
Contour Transformer Network for One-shot Segmentation of Anatomical StructuresCode0
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract ReasoningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified