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 126–150 of 305 papers

TitleStatusHype
HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional Computing—0
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash FunctionsCode0
Harnessing Geometric Constraints from Emotion Labels to improve Face Verification—0
One Line To Rule Them All: Generating LO-Shot Soft-Label PrototypesCode1
One-shot learning for the long term: consolidation with an artificial hippocampal algorithm—0
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries—0
Echo-SyncNet: Self-supervised Cardiac View Synchronization in EchocardiographyCode1
Unsupervised Noisy Tracklet Person Re-identification—0
Better Together: Resnet-50 accuracy with 13 fewer parameters and at 3 speed—0
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks—0
Robust One Shot Audio to Video Generation—0
One-Shot Learning with Triplet Loss for Vegetation Classification Tasks—0
Contour Transformer Network for One-shot Segmentation of Anatomical StructuresCode0
ScaleNAS: One-Shot Learning of Scale-Aware Representations for Visual Recognition—0
Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model—0
Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code—0
Resource Constrained Dialog Policy Learning via Differentiable Inductive Logic Programming—0
One-Shot Federated Learning with Neuromorphic Processors—0
Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal ArchitectureCode0
One-shot Learning for Temporal Knowledge Graphs—0
Learning from similarity and information extraction from structured documentsCode0
Contour Primitive of Interest Extraction Network Based on One-Shot Learning for Object-Agnostic Vision Measurement—0
One-Shot learning based classification for segregation of plastic waste—0
Distilled One-Shot Federated Learning—0
'Less Than One'-Shot Learning: Learning N Classes From M<N SamplesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified