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 1151–1175 of 3569 papers

TitleStatusHype
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Meta Generative Attack on Person Reidentification—0
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management—0
Hyperparameter Optimization as a Service on INFN CloudCode0
A Comprehensive Survey of Dataset Distillation—0
Thompson Sampling with Diffusion Generative Prior—0
Optimistic Meta-Gradients—0
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action RecognitionCode1
Unsupervised Learning for Combinatorial Optimization Needs Meta-LearningCode0
Few-shot Node Classification with Extremely Weak SupervisionCode0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Task Weighting in Meta-learning with Trajectory Optimisation—0
A Meta-Learning Algorithm for Interrogative Agendas—0
Tracking Brand-Associated Polarity-Bearing Topics in User ReviewsCode0
Metalearning generalizable dynamics from trajectories—0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan—0
s-Adaptive Decoupled Prototype for Few-Shot Object Detection—0
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction—0
A Simple Recipe to Meta-Learn Forward and Backward Transfer—0
Global Perception Based Autoregressive Neural Processes—0
MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding From Object DetectionCode1
Bi-Level Meta-Learning for Few-Shot Domain Generalization—0
Deep Random Projector: Accelerated Deep Image PriorCode1
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level—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