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 301325 of 3569 papers

TitleStatusHype
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label MiscorrectionCode1
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance FieldCode1
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo AnchorsCode1
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-SpeechCode1
RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity RecognitionCode1
Meta-Knowledge Transfer for Inductive Knowledge Graph EmbeddingCode1
Learning where to learn: Gradient sparsity in meta and continual learningCode1
Meta-Learning Sparse Implicit Neural RepresentationsCode1
Meta-learning with an Adaptive Task SchedulerCode1
On sensitivity of meta-learning to support dataCode1
A Closer Look at Few-Shot Video Classification: A New Baseline and BenchmarkCode1
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic SegmentationCode1
Exploiting Domain-Specific Features to Enhance Domain GeneralizationCode1
Meta-learning via Language Model In-context TuningCode1
On the Convergence Theory for Hessian-Free Bilevel AlgorithmsCode1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
Meta-Learning with Task-Adaptive Loss Function for Few-Shot LearningCode1
Graph Meta Network for Multi-Behavior RecommendationCode1
Meta Internal LearningCode1
Influence-Balanced Loss for Imbalanced Visual ClassificationCode1
Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide SusceptibilityCode1
Meta Learning on a Sequence of Imbalanced Domains with Difficulty AwarenessCode1
An Enhanced Span-based Decomposition Method for Few-Shot Sequence LabelingCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
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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