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 1826–1850 of 3569 papers

TitleStatusHype
MAML-CL: Edited Model-Agnostic Meta-Learning for Continual LearningCode0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Learn to Adapt for Generalized Zero-Shot Text Classification—0
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification—0
Online Meta Adaptation for Variable-Rate Learned Image Compression—0
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control—0
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning—0
Learning to Evolve on Dynamic GraphsCode0
Learning Online for Unified Segmentation and Tracking Models—0
Attention Guided Cosine Margin For Overcoming Class-Imbalance in Few-Shot Road Object DetectionCode1
OSSEM: one-shot speaker adaptive speech enhancement using meta learning—0
Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understanding—0
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning—0
Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation—0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-SpeechCode1
Meta Cross-Modal Hashing on Long-Tailed Data—0
MetaMIML: Meta Multi-Instance Multi-Label Learning—0
Crowdsourcing with Meta-Workers: A New Way to Save the Budget—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Meta-learning for RIS-assisted NOMA Networks—0
Graph neural network initialisation of quantum approximate optimisation—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