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 2076–2100 of 3569 papers

TitleStatusHype
Towards Sharper Information-theoretic Generalization Bounds for Meta-Learning—0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Towards Subject Agnostic Affective Emotion Recognition—0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search—0
Towards Understanding Generalization in Gradient-Based Meta-Learning—0
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Towards Zero-Shot Learning with Fewer Seen Class Examples—0
Tracking by Instance Detection: A Meta-Learning Approach—0
Training an Interactive Helper—0
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization—0
Training few-shot classification via the perspective of minibatch and pretraining—0
Trajectory-Based Meta-Learning for Out-Of-Vocabulary Word Embedding Learning—0
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning—0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
Transfer-based Adversarial Poisoning Attacks for Online (MIMO-)Deep Receviers—0
Transfering Hierarchical Structure with Dual Meta Imitation Learning—0
Transfer Learning for Algorithm Recommendation—0
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning—0
Transfer Learning for Finetuning Large Language Models—0
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users—0
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization—0
Transferring Hierarchical Structure with Dual Meta Imitation Learning—0
Transferring SLU Models in Novel Domains—0
Transformation Invariant Few-Shot Object 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