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
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features—0
Large-Scale Meta-Learning with Continual Trajectory Shifting—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization—0
LEAF: A Benchmark for Federated Settings—0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learning a Better Initialization for Soft Prompts via 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