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 26–50 of 305 papers

TitleStatusHype
Efficient Cross-Validation of Echo State NetworksCode1
One Line To Rule Them All: Generating LO-Shot Soft-Label PrototypesCode1
One-Shot Learning for Semantic SegmentationCode1
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel NoisesCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
UOD: Universal One-shot Detection of Anatomical LandmarksCode1
'Less Than One'-Shot Learning: Learning N Classes From M<N SamplesCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
Matching Networks for One Shot LearningCode1
Differentiable Wavetable SynthesisCode1
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot LearningCode0
Improving Siamese Networks for One Shot Learning using Kernel Based Activation functionsCode0
One-Shot Collaborative Data DistillationCode0
It's DONE: Direct ONE-shot learning with quantile weight imprintingCode0
Learning from similarity and information extraction from structured documentsCode0
Active Use of Latent Constituency Representation in both Humans and Large Language ModelsCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image RecognitionCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract ReasoningCode0
A Novel Embedding Architecture and Score Level Fusion Scheme for Occluded Image Acquisition in Ear Biometrics SystemCode0
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash FunctionsCode0
Generalization in Machine Learning via Analytical Learning TheoryCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Show:102550
← PrevPage 2 of 13Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified