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 101–125 of 305 papers

TitleStatusHype
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN ImagesCode0
Siamese neural networks for one-shot image recognitionCode0
Tab2KG: Semantic Table Interpretation with Lightweight Semantic ProfilesCode0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
Latent Attention For If-Then Program Synthesis—0
Assessing Shape Bias Property of Convolutional Neural Networks—0
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
Investigation of using disentangled and interpretable representations with language conditioning for cross-lingual voice conversion—0
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
ARTiS: Appearance-based Action Recognition in Task Space for Real-Time Human-Robot Collaboration—0
Inverting The Generator Of A Generative Adversarial Network—0
Interactive Instance Annotation with Siamese Networks—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
AROS: Affordance Recognition with One-Shot Human Stances—0
Learning feed-forward one-shot learners—0
Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction—0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining—0
Improving One-Shot Learning through Fusing Side Information—0
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified