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 151–175 of 305 papers

TitleStatusHype
DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations—0
One-shot Text Field Labeling using Attention and Belief Propagation for Structure Information ExtractionCode1
Grounded Language Learning Fast and SlowCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
Exploiting Temporal Coherence for Self-Supervised One-shot Video Re-identification—0
Sorted Pooling in Convolutional Networks for One-shot Learning—0
One-Shot Learning for Language ModellingCode1
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching—0
Learning to Segment Anatomical Structures Accurately from One Exemplar—0
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
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
Typilus: Neural Type HintsCode1
One-Shot GAN Generated Fake Face Detection—0
Compositional Embeddings for Multi-Label One-Shot Learning—0
Uncertainty-based Modulation for Lifelong Learning—0
A One-Shot Learning Framework for Assessment of Fibrillar Collagen from Second Harmonic Generation Images of an Infarcted Myocardium—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified