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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

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Showing 301–350 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
Graph Meta Network for Multi-Behavior RecommendationCode1
Meta-Learning with Task-Adaptive Loss Function for Few-Shot LearningCode1
Influence-Balanced Loss for Imbalanced Visual ClassificationCode1
Meta Internal LearningCode1
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
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural NetworksCode1
Gradient Imitation Reinforcement Learning for Low Resource Relation ExtractionCode1
Cross-Market Product RecommendationCode1
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-LearningCode1
Exploring Task Difficulty for Few-Shot Relation ExtractionCode1
Knowledge-Aware Meta-learning for Low-Resource Text ClassificationCode1
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-LearningCode1
Few-shot Visual Relationship Co-localizationCode1
Meta Self-Learning for Multi-Source Domain Adaptation: A BenchmarkCode1
Relational Embedding for Few-Shot ClassificationCode1
Prototype Completion for Few-Shot LearningCode1
Meta Gradient Adversarial AttackCode1
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight TransformerCode1
Dynamic Relevance Learning for Few-Shot Object DetectionCode1
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text ClassificationCode1
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
ProtoTransformer: A Meta-Learning Approach to Providing Student FeedbackCode1
Neural Fixed-Point Acceleration for Convex OptimizationCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
A Channel Coding Benchmark for Meta-LearningCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Memory Efficient Meta-Learning with Large ImagesCode1
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified