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 71–80 of 305 papers

TitleStatusHype
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Abstracted Gaussian Prototypes for One-Shot Concept LearningCode0
Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness GuaranteesCode0
Image Deformation Meta-Networks for One-Shot LearningCode0
Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction ModelsCode0
Attentive Recurrent ComparatorsCode0
Generalization in Machine Learning via Analytical Learning TheoryCode0
Few-Shot Adversarial Learning of Realistic Neural Talking Head ModelsCode0
Emulating Brain-like Rapid Learning in Neuromorphic Edge ComputingCode0
Few-Shot Knowledge Graph CompletionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified