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 831–840 of 3569 papers

TitleStatusHype
MAD: Meta Adversarial Defense BenchmarkCode0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
Generalizable Neural Fields as Partially Observed Neural Processes—0
Convergence of Gradient-based MAML in LQR—0
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
A supervised generative optimization approach for tabular data—0
Retrieval-Augmented Meta Learning for Low-Resource Text Classification—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—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