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 2301–2310 of 3569 papers

TitleStatusHype
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search—0
From Biased Data to Unbiased Models: a Meta-Learning Approach—0
Directional Domain Generalization—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Discrepancy-Optimal Meta-Learning for Domain Generalization—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Learning to Learn across Diverse Data Biases in Deep Face Recognition—0
FedNAS: Federated Deep Learning via Neural Architecture Search—0
Practical Conditional Neural Process Via Tractable Dependent Predictions—0
Learning Neural Processes on the Fly—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