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 51–75 of 305 papers

TitleStatusHype
Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget ExperiencesCode0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Active Use of Latent Constituency Representation in both Humans and Large Language ModelsCode0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
Contour Transformer Network for One-shot Segmentation of Anatomical StructuresCode0
Learning to Remember Rare EventsCode0
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
One-shot Detail Retouching with Patch Space Neural Transformation BlendingCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Learning from similarity and information extraction from structured documentsCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Active One-shot LearningCode0
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot LearningCode0
BAE-NET: Branched Autoencoder for Shape Co-SegmentationCode0
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional ClassifierCode0
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image RecognitionCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
Few-Shot Knowledge Graph CompletionCode0
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis TreatmentCode0
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash FunctionsCode0
Generalization in Machine Learning via Analytical Learning TheoryCode0
Improving Siamese Networks for One Shot Learning using Kernel Based Activation functionsCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
Abstracted Gaussian Prototypes for One-Shot Concept LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified