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 201–210 of 305 papers

TitleStatusHype
Adaptive Image Transformer for One-Shot Object Detection—0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition—0
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning—0
Meta-Reinforcement Learning with Self-Modifying Networks—0
AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking—0
AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining—0
A Hippocampus Model for Online One-Shot Storage of Pattern Sequences—0
A Model of Zero-Shot Learning of Spoken Language Understanding—0
An Event based Prediction Suffix Tree—0
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Siamese Neural NetworkAccuracy97.5—Unverified