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 301310 of 3569 papers

TitleStatusHype
Few-Shot One-Class Classification via Meta-LearningCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
Adapting to Distribution Shift by Visual Domain Prompt GenerationCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
BlackGoose Rimer: Harnessing RWKV-7 as a Simple yet Superior Replacement for Transformers in Large-Scale Time Series ModelingCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo SimulationCode1
BOML: A Modularized Bilevel Optimization Library in Python for Meta LearningCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
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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