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 31513160 of 3569 papers

TitleStatusHype
Effective Few-Shot Named Entity Linking by Meta-LearningCode0
Adaptive Prior Selection for Repertoire-based Online Adaptation in RoboticsCode0
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot TasksCode0
Natural Language to Structured Query Generation via Meta-LearningCode0
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action RankerCode0
Reward Design for Reinforcement Learning AgentsCode0
Meta-Learners for Few-Shot Weakly-Supervised Medical Image SegmentationCode0
Dynamic Multimodal Fusion via Meta-Learning Towards Micro-Video RecommendationCode0
Rewarded meta-pruning: Meta Learning with Rewards for Channel PruningCode0
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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