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 31–40 of 305 papers

TitleStatusHype
Active Use of Latent Constituency Representation in both Humans and Large Language ModelsCode0
Response Matching for generating materials and molecules—0
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media—0
Learning Symbolic Task Representation from a Human-Led Demonstration: A Memory to Store, Retrieve, Consolidate, and Forget ExperiencesCode0
Exploring the potential of prototype-based soft-labels data distillation for imbalanced data classification—0
A Feature-based Generalizable Prediction Model for Both Perceptual and Abstract ReasoningCode0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
More than the Sum of Its Parts: Ensembling Backbone Networks for Few-Shot Segmentation—0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining—0
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified