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 3091–3100 of 3569 papers

TitleStatusHype
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Interactive Graph Convolutional Filtering—0
Interactive singing melody extraction based on active adaptation—0
Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform—0
Interpretable Concept-based Prototypical Networks for Few-Shot Learning—0
Interpretable Deep Convolutional Neural Networks via Meta-learning—0
Interpretable Meta-Learning of Physical Systems—0
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning—0
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Invariant Feature Learning for Sensor-based Human Activity Recognition—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