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 276300 of 3569 papers

TitleStatusHype
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IVCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Covariate Distribution Aware Meta-learningCode1
Control-oriented meta-learningCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
CURI: A Benchmark for Productive Concept Learning Under UncertaintyCode1
Curriculum-Meta Learning for Order-Robust Continual Relation ExtractionCode1
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective AdaptationCode1
AReLU: Attention-based Rectified Linear UnitCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
A picture of the space of typical learnable tasksCode1
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object DetectionCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Diffusion-Based Neural Network Weights GenerationCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot RelationsCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
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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