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

Showing 261270 of 3569 papers

TitleStatusHype
Concept Learners for Few-Shot LearningCode1
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Bayesian Model-Agnostic Meta-LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified