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 101–150 of 305 papers

TitleStatusHype
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI—0
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification—0
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing—0
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing—0
High-dimensional separability for one- and few-shot learning—0
Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis—0
Improving One-Shot Learning through Fusing Side Information—0
AROS: Affordance Recognition with One-Shot Human Stances—0
InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input—0
Multi-Attention Network for One Shot Learning—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
Correlation Weighted Prototype-based Self-Supervised One-Shot Segmentation of Medical Images—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
Multimodal Continuation-style Architectures for Human-Robot Interaction—0
Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images—0
Learning feed-forward one-shot learners—0
Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction—0
Discovering objects and their relations from entangled scene representations—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances—0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
One-Shot Learning for Text-to-SQL Generation—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
Modeling Time Series Similarity with Siamese Recurrent Networks—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Learning to Segment Anatomical Structures Accurately from One Exemplar—0
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model—0
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation—0
Demystification of Few-shot and One-shot Learning—0
Memory Matching Networks for One-Shot Image Recognition—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
Metalearning with Hebbian Fast Weights—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations—0
Mathematics of Digital Twins and Transfer Learning for PDE Models—0
MAVOT: Memory-Augmented Video Object Tracking—0
Learning similarity measures from data—0
Max-Margin Invariant Features from Transformed Unlabelled Data—0
Measuring Immediate Adaptation Performance for Neural Machine Translation—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified