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 11511175 of 3569 papers

TitleStatusHype
Learning Fast Adaptation with Meta Strategy OptimizationCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained LearningCode0
Anti-Retroactive Interference for Lifelong LearningCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Joint inference and input optimization in equilibrium networksCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Interpretable Meta-Measure for Model PerformanceCode0
Adversarial Attacks on Graph Neural Networks via Meta LearningCode0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Few-Shot Learning with Global Class RepresentationsCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
FIGR: Few-shot Image Generation with ReptileCode0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
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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