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 13111320 of 3569 papers

TitleStatusHype
Cross-Domain Few-Shot Learning with Meta Fine-Tuning0
A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems0
A Heuristic Search Algorithm Using the Stability of Learning Algorithms in Certain Scenarios as the Fitness Function: An Artificial General Intelligence Engineering Approach0
Guided Evolutionary Strategies: Escaping the curse of dimensionality in random search0
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition0
Guided Variational Autoencoder for Disentanglement Learning0
Incremental Meta-Learning via Indirect Discriminant Alignment0
A Simple Recipe to Meta-Learn Forward and Backward Transfer0
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts0
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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