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 31–40 of 305 papers

TitleStatusHype
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
BaseTransformers: Attention over base data-points for One Shot LearningCode1
Differentiable Wavetable SynthesisCode1
EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot LearningCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
Few-Shot Knowledge Graph CompletionCode0
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash FunctionsCode0
One-Shot Collaborative Data DistillationCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
Show:102550
← PrevPage 4 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified