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 2661–2670 of 3569 papers

TitleStatusHype
Probabilistic Meta-Learning for Bayesian Optimization—0
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning—0
Learning to Recover from Failures using Memory—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
Optimal allocation of data across training tasks in meta-learning—0
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior—0
Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
MM-FSOD: Meta and metric integrated few-shot object detection—0
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering—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