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 251–260 of 305 papers

TitleStatusHype
Generalization in Machine Learning via Analytical Learning TheoryCode0
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-LearningCode0
Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters—0
Weakly Supervised One-Shot Detection with Attention Similarity Networks—0
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING—0
Make SVM great again with Siamese kernel for few-shot learningCode0
Max-Margin Invariant Features from Transformed Unlabelled Data—0
MAVOT: Memory-Augmented Video Object Tracking—0
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
Max-Margin Invariant Features from Transformed Unlabeled Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified