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 1601–1625 of 3569 papers

TitleStatusHype
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise—0
Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images—0
A Meta-Learning Approach to Predicting Performance and Data Requirements—0
Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness—0
Bayesian Active Meta-Learning for Black-Box Optimization—0
Dynamic Channel Access via Meta-Reinforcement Learning—0
Dynamic backdoor attacks against federated learning—0
A Meta-Learning Approach to Population-Based Modelling of Structures—0
Adaptive Hierarchical Hyper-gradient Descent—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan—0
A Meta-Learning Approach to One-Step Active Learning—0
Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification—0
A Meta Learning Approach to Grammatical Error Correction—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
A Comprehensive Review of Few-shot Action Recognition—0
DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization—0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction—0
Backdoor Attacks on Federated Meta-Learning—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning—0
Auxiliary task discovery through generate-and-test—0
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks—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