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

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Showing 61–70 of 305 papers

TitleStatusHype
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Active One-shot LearningCode0
Generalization in Machine Learning via Analytical Learning TheoryCode0
BAE-NET: Branched Autoencoder for Shape Co-SegmentationCode0
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional ClassifierCode0
Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image RecognitionCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Few-Shot Knowledge Graph CompletionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified