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 71–80 of 305 papers

TitleStatusHype
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning—0
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task—0
SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation—0
An Event based Prediction Suffix Tree—0
Temporal credit assignment for one-shot learning utilizing a phase transition material—0
Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data—0
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Bias Testing and Mitigation in LLM-based Code Generation—0
Towards One-Shot Learning for Text Classification using Inductive Logic ProgrammingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified