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 161–170 of 305 papers

TitleStatusHype
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
Efficient implementations of echo state network cross-validationCode1
Adjoined Networks: A Training Paradigm with Applications to Network CompressionCode1
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking—0
Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples—0
One-Shot Recognition of Manufacturing Defects in Steel SurfacesCode1
One-Shot Image Classification by Learning to Restore Prototypes—0
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review—0
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-MemoryCode0
Meta-Meta Classification for One-Shot LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified