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 2676–2700 of 3569 papers

TitleStatusHype
Discrepancy-Optimal Meta-Learning for Domain Generalization—0
Discrete Infomax Codes for Supervised Representation Learning—0
Discrete InfoMax Codes for Meta-Learning—0
Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning—0
Distilling Symbolic Priors for Concept Learning into Neural Networks—0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Distributed Estimation by Two Agents with Different Feature Spaces—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks—0
Distributed Representations of Words and Documents for Discriminating Similar Languages—0
Task-Robust Model-Agnostic Meta-Learning—0
Distributionally robust minimization in meta-learning for system identification—0
Distribution Embedding Network for Meta-Learning with Variable-Length Input—0
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks—0
Divergent Search for Few-Shot Image Classification—0
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP—0
Diverse Inference and Verification for Advanced Reasoning—0
DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing—0
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition—0
DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Domain Adaptation in Dialogue Systems using Transfer and Meta-Learning—0
Domain Agnostic Few-Shot Learning For Document Intelligence—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