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 3131–3140 of 3569 papers

TitleStatusHype
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Reusable Options through Gradient-based Meta LearningCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual EffectsCode0
Multi-task Maximum Entropy Inverse Reinforcement LearningCode0
Meta-Information Guided Meta-Learning for Few-Shot Relation ClassificationCode0
Multi-Task Meta Learning: learn how to adapt to unseen tasksCode0
Meta-INR: Efficient Encoding of Volumetric Data via Meta-Learning Implicit Neural RepresentationCode0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
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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