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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2526–2550 of 3569 papers

TitleStatusHype
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Meta-Adaptive Prompt Distillation for Few-Shot Visual Question Answering—0
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing—0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)—0
Meta-aprendizado para otimizacao de parametros de redes neurais—0
Meta Arcade: A Configurable Environment Suite for Meta-Learning—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Meta Attention For Off-Policy Actor-Critic—0
Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms—0
MetaAugment: Sample-Aware Data Augmentation Policy Learning—0
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations—0
Meta Auxiliary Learning for Facial Action Unit Detection—0
Meta-Auxiliary Network for 3D GAN Inversion—0
Meta Back-translation—0
MetaBags: Bagged Meta-Decision Trees for Regression—0
Meta Balanced Network for Fair Face Recognition—0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
Towards Unbiased Calibration using Meta-Regularization—0
MetaCap: Meta-learning Priors from Multi-View Imagery for Sparse-view Human Performance Capture and Rendering—0
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population—0
MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples—0
MetaComp: Learning to Adapt for Online Depth Completion—0
Meta Compositional Referring Expression Segmentation—0
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning—0
Meta-Continual Learning of Neural Fields—0
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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