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 351375 of 3569 papers

TitleStatusHype
Procedural generation of meta-reinforcement learning tasksCode1
A Closer Look at Few-Shot Video Classification: A New Baseline and BenchmarkCode1
A Large Scale Search Dataset for Unbiased Learning to RankCode1
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
Graph Meta Learning via Local SubgraphsCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
Attentional-Biased Stochastic Gradient DescentCode1
Group Preference Optimization: Few-Shot Alignment of Large Language ModelsCode1
GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View SynthesisCode1
HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement LearningCode1
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
How to train your MAMLCode1
Bayesian Model-Agnostic Meta-LearningCode1
Hypernetworks build Implicit Neural Representations of SoundsCode1
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action RecognitionCode1
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype EnhancementCode1
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level TransferCode1
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-RankingCode1
Incremental Few-Shot Object Detection via Simple Fine-Tuning ApproachCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
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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