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 1591–1600 of 3569 papers

TitleStatusHype
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization—0
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
A Simple Approach to Adversarial Robustness in Few-shot Image ClassificationCode0
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification—0
Neuronal diversity can improve machine learning for physics and beyondCode0
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual LearningCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Pin the Memory: Learning to Generalize Semantic SegmentationCode1
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—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