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 726–750 of 3569 papers

TitleStatusHype
Scalable Meta-Learning with Gaussian Processes—0
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor—0
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers—0
TIDE: Test Time Few Shot Object DetectionCode0
MetaDefa: Meta-learning based on Domain Enhancement and Feature Alignment for Single Domain Generalization—0
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
SEGIC: Unleashing the Emergent Correspondence for In-Context SegmentationCode1
Task-Distributionally Robust Data-Free Meta-Learning—0
Enhancing Peak Assignment in 13C NMR Spectroscopy: A Novel Approach Using Multimodal Alignment—0
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty PredictionCode1
MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learningCode1
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Decoupled DETR For Few-shot Object Detection—0
Unraveling the Control Engineer's Craft with Neural Networks—0
On the Communication Complexity of Decentralized Bilevel Optimization—0
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing PlatformsCode0
Meta-DSP: A Meta-Learning Approach for Data-Driven Nonlinear Compensation in High-Speed Optical Fiber Systems—0
Concept-free Causal Disentanglement with Variational Graph Auto-EncoderCode0
Adaptive Optimization Algorithms for Machine Learning—0
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning—0
Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction—0
In-Context Learning for MIMO Equalization Using Transformer-Based Sequence ModelsCode0
Latent Task-Specific Graph Network SimulatorsCode0
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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