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 81–90 of 3569 papers

TitleStatusHype
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained ModelsCode1
Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-LearningCode1
NTK-Guided Few-Shot Class Incremental LearningCode1
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
Harnessing Meta-Learning for Improving Full-Frame Video StabilizationCode1
On Latency Predictors for Neural Architecture SearchCode1
Fast and Efficient Local Search for Genetic Programming Based Loss Function LearningCode1
Diffusion-Based Neural Network Weights GenerationCode1
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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