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 371380 of 3569 papers

TitleStatusHype
BERT Learns to Teach: Knowledge Distillation with Meta LearningCode1
Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain DetectionCode1
Write like you: Synthesizing your cursive online Chinese handwriting via metric-based meta learningCode1
Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and BeyondCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and AdvertisingCode1
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
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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