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 231–240 of 305 papers

TitleStatusHype
Compositional Embeddings for Multi-Label One-Shot Learning—0
Compositional Embeddings: Joint Perception and Comparison of Class Label Sets—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Continual One-Shot Learning of Hidden Spike-Patterns with Neural Network Simulation Expansion and STDP Convergence Predictions—0
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries—0
Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement—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
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified