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 311–320 of 3569 papers

TitleStatusHype
Generalizable and Robust Spectral Method for Multi-view Representation LearningCode0
Learning Where to Edit Vision TransformersCode0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Teaching Models to Improve on Tape—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Transfer Learning for Finetuning Large Language Models—0
MADOD: Generalizing OOD Detection to Unseen Domains via G-Invariance Meta-Learning—0
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
FEED: Fairness-Enhanced Meta-Learning for Domain Generalization—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