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 2151–2175 of 3569 papers

TitleStatusHype
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Meta-Learning with Variational Semantic Memory for Word Sense DisambiguationCode0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Write like you: Synthesizing your cursive online Chinese handwriting via metric-based meta learningCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
Light Field Networks: Neural Scene Representations with Single-Evaluation RenderingCode1
Minimax and Neyman-Pearson Meta-Learning for Outlier LanguagesCode0
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and BeyondCode1
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Modality-specific Distillation—0
Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement (Meta-learning : Classifying Messages into Unseen Emotional Categories)—0
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks—0
DReCa: A General Task Augmentation Strategy for Few-Shot Natural Language Inference—0
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning—0
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and AdvertisingCode1
Meta-HAR: Federated Representation Learning for Human Activity RecognitionCode1
Efficient and Modular Implicit DifferentiationCode2
BaMBNet: A Blur-aware Multi-branch Network for Defocus DeblurringCode1
Energy-Efficient and Federated Meta-Learning via Projected Stochastic Gradient Ascent—0
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning—0
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Towards mental time travel: a hierarchical memory for reinforcement learning agentsCode1
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningCode1
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—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