SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1541–1550 of 3569 papers

TitleStatusHype
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Convolutional Neural Processes for Inpainting Satellite Images—0
Semi-Parametric Inducing Point Networks and Neural ProcessesCode0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in MachinesCode0
Meta-Learning Regrasping Strategies for Physical-Agnostic Objects—0
Should Models Be Accurate?—0
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments—0
FIND:Explainable Framework for Meta-learning—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified