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 226–250 of 305 papers

TitleStatusHype
O2A: One-shot Observational learning with Action vectors—0
Towards one-shot learning for rare-word translation with external experts—0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing—0
On the Adaptability of Unsupervised CNN-Based Deformable Image Registration to Unseen Image Domains—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
Object Relation Detection Based on One-shot Learning—0
Metalearning with Hebbian Fast Weights—0
One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary VideoCode0
One-shot Learning for Question-Answering in Gaokao History ChallengeCode0
Dynamic Spectrum Matching with One-shot Learning—0
One-Shot Action Localization by Learning Sequence Matching Network—0
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning—0
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