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 176–200 of 305 papers

TitleStatusHype
Improving One-Shot Learning through Fusing Side Information—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Interactive Instance Annotation with Siamese Networks—0
Inverting The Generator Of A Generative Adversarial Network—0
Investigation of using disentangled and interpretable representations with language conditioning for cross-lingual voice conversion—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
Latent Attention For If-Then Program Synthesis—0
Learning feed-forward one-shot learners—0
Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction—0
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances—0
Learning Non-deterministic Representations with Energy-based Ensembles—0
Learning Relational Representations by Analogy using Hierarchical Siamese Networks—0
Learning similarity measures from data—0
Learning to Segment Anatomical Structures Accurately from One Exemplar—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
Lifelong Machine Learning for Topic Modeling and Beyond—0
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task—0
LLMs are One-Shot URL Classifiers and Explainers—0
Low Data Drug Discovery with One-shot Learning—0
Mathematics of Digital Twins and Transfer Learning for PDE Models—0
MAVOT: Memory-Augmented Video Object Tracking—0
Max-Margin Invariant Features from Transformed Unlabeled Data—0
Max-Margin Invariant Features from Transformed Unlabelled Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified