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
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
Learn to Adapt for Generalized Zero-Shot Text ClassificationCode1
Learning to Scaffold: Optimizing Model Explanations for TeachingCode1
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot SegmentationCode1
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
Self-Guided Learning to Denoise for Robust RecommendationCode1
Control-oriented meta-learningCode1
Pin the Memory: Learning to Generalize Semantic SegmentationCode1
MetaAudio: A Few-Shot Audio Classification BenchmarkCode1
Few-Shot Class-Incremental Learning by Sampling Multi-Phase TasksCode1
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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