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 1061–1070 of 3569 papers

TitleStatusHype
Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce ScenariosCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Joint inference and input optimization in equilibrium networksCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
Show:102550
← PrevPage 107 of 357Next →
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