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 3111–3120 of 3569 papers

TitleStatusHype
Learning to reinforcement learn for Neural Architecture SearchCode0
Fair Meta-Learning: Learning How to Learn Fairly—0
Shaping Visual Representations with Language for Few-shot ClassificationCode0
Addressing Ambiguity of Emotion Labels Through Meta-Learning—0
Learning One-Shot Imitation from Humans without HumansCode0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Seeded self-play for language learning—0
Learning to Customize Model Structures for Few-shot Dialogue Generation TasksCode0
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines—0
Hierarchical Expert Networks for 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