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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 305 papers

TitleStatusHype
One-Shot Learning on Attributed Sequences—0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
One-Shot Learning from a Demonstration with Hierarchical Latent Language—0
Brain-inspired Cognition in Next Generation Racetrack Memories—0
Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-LearningCode0
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN—0
AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language ModelsCode0
High-dimensional separability for one- and few-shot learning—0
Adaptive Image Transformer for One-Shot Object Detection—0
One-shot learning of paired association navigation with biologically plausible schemasCode0
One-shot Learning with Absolute GeneralizationCode0
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing—0
One-shot learning for acoustic identification of bird species in non-stationary environments—0
Updatable Siamese Tracker with Two-stage One-shot Learning—0
Watershed of Artificial Intelligence: Human Intelligence, Machine Intelligence, and Biological Intelligence—0
Demystification of Few-shot and One-shot Learning—0
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep LearningCode0
One-shot learning for solution operators of partial differential equations—0
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-shot learning for the long term: consolidation with an artificial hippocampal algorithm—0
Continuous Learning in Neural Machine Translation using Bilingual Dictionaries—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified