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

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Showing 1331–1340 of 3569 papers

TitleStatusHype
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive LearningCode0
Improving Meta-Continual Learning Representations with Representation ReplayCode0
Cross-Modal Generalization: Learning in Low Resource Modalities via Meta-AlignmentCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the TasksCode0
Meta-Learning with Shared Amortized Variational InferenceCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
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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