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 651–675 of 3569 papers

TitleStatusHype
A Simple Approach to Case-Based Reasoning in Knowledge BasesCode1
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-LearningCode1
Cross-Domain Few-Shot Classification via Adversarial Task AugmentationCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Can Gradient Descent Simulate Prompting?—0
CAML: Fast Context Adaptation via Meta-Learning—0
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation—0
Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction—0
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
CAFENet: Class-Agnostic Few-Shot Edge Detection Network—0
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification—0
Analytic Personalized Federated Meta-Learning—0
Adaptive Uncertainty Quantification for Scenario-based Control Using Meta-learning of Bayesian Neural Networks—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Budget-aware Few-shot Learning via Graph Convolutional Network—0
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach—0
Analogy-Forming Transformers for Few-Shot 3D Parsing—0
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning—0
Adaptive Task Sampling for Meta-Learning—0
Bridging Pattern-Aware Complexity with NP-Hard Optimization: A Unifying Framework and Empirical Study—0
Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization—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