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 1021–1030 of 3569 papers

TitleStatusHype
Fast-adapting and Privacy-preserving Federated Recommender System—0
Fast Adaptive Anomaly Detection—0
Automatic Forecasting via Meta-Learning—0
A Markov Decision Process Approach to Active Meta Learning—0
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Automatic Combination of Sample Selection Strategies for Few-Shot Learning—0
Discrete InfoMax Codes for Meta-Learning—0
Discrete Infomax Codes for Supervised Representation Learning—0
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition—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