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 2821–2830 of 3569 papers

TitleStatusHype
Using Sensory Time-cue to enable Unsupervised Multimodal Meta-learning—0
SML: Semantic Meta-learning for Few-shot Semantic Segmentation—0
Teaching to Learn: Sequential Teaching of Agents with Inner States—0
A Markov Decision Process Approach to Active Meta Learning—0
Meta-learning based Alternating Minimization Algorithm for Non-convex OptimizationCode0
Proxy Network for Few Shot LearningCode0
Information Theoretic Meta Learning with Gaussian Processes—0
A Survey on Machine Learning from Few Samples—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—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