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

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Showing 241–250 of 305 papers

TitleStatusHype
Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System—0
A Generative Model For Electron Paths—0
Dynamic Few-Shot Visual Learning without ForgettingCode1
Memory Matching Networks for One-Shot Image Recognition—0
Deep Triplet Ranking Networks for One-Shot RecognitionCode0
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach—0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
MemGEN: Memory is All You Need—0
Assessing Shape Bias Property of Convolutional Neural Networks—0
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional ClassifierCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified