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 1401–1425 of 3569 papers

TitleStatusHype
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Improve Noise Tolerance of Robust Loss via Noise-Awareness—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity—0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
Improving both domain robustness and domain adaptability in machine translation—0
Contrastive Conditional Neural Processes—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks—0
Large-Scale Meta-Learning with Continual Trajectory Shifting—0
Generalization in Neural Networks: A Broad Survey—0
Generalizable speech deepfake detection via meta-learned LoRA—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Decoupled Pronunciation and Prosody Modeling in Meta-Learning-Based Multilingual Speech Synthesis—0
Generalizable Person Re-Identification by Domain-Invariant Mapping Network—0
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation—0
Continuous Learning in a Hierarchical Multiscale Neural Network—0
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift—0
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?—0
Improving the Reliability for Confidence Estimation—0
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks—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