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 3426–3450 of 3569 papers

TitleStatusHype
CAML: Fast Context Adaptation via Meta-Learning—0
Sample Efficient Adaptive Text-to-Speech—0
Meta Learning with Fast/Slow Learners—0
AutoLoss: Learning Discrete Schedule for Alternate Optimization—0
Correction Networks: Meta-Learning for Zero-Shot Learning—0
A Meta-Learning Approach for Custom Model Training—0
Open-world Learning and Application to Product ClassificationCode0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Learning Invariances for Policy GeneralizationCode0
Few-Shot Human Motion Prediction via Meta-Learning—0
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning—0
Angular-Based Word Meta-Embedding Learning—0
Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss WeightingCode0
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary DataCode0
Meta-learning autoencoders for few-shot predictionCode0
Meta-Learning Priors for Efficient Online Bayesian RegressionCode0
RARD II: The 94 Million Related-Article Recommendation Dataset—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Variance Reduction for Reinforcement Learning in Input-Driven Environments—0
DiCE: The Infinitely Differentiable Monte Carlo EstimatorCode0
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel LearningCode0
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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