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

Showing 10411050 of 3569 papers

TitleStatusHype
A Simple Neural Attentive Meta-LearnerCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Cross-Domain Few-Shot Graph ClassificationCode0
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference LearningCode0
Cross-Domain Continual Learning via CLAMPCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified