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

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Showing 3361–3370 of 3569 papers

TitleStatusHype
Where Do Human Heuristics Come From?—0
Adaptive Cross-Modal Few-Shot LearningCode0
Fast Efficient Hyperparameter Tuning for Policy GradientsCode0
Task2Vec: Task Embedding for Meta-LearningCode0
Adaptive Posterior Learning: few-shot learning with a surprise-based memory moduleCode0
FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning—0
A Meta-Transfer Objective for Learning to Disentangle Causal MechanismsCode0
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networksCode0
Reward Shaping via Meta-Learning—0
Few-shot Learning with Meta Metric Learners—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