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 111–120 of 305 papers

TitleStatusHype
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 Models—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified