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 1376–1400 of 3569 papers

TitleStatusHype
Hyperparameter Optimization in Machine Learning—0
Continuous-Time Meta-Learning with Forward Mode Differentiation—0
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks—0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Knowledge Consolidation based Class Incremental Online Learning with Limited Data—0
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning—0
Identifying Physical Law of Hamiltonian Systems via Meta-Learning—0
Generalization in Neural Networks: A Broad Survey—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
Image Retrieval And Classification Using Local Feature Vectors—0
Imbalanced Classification via Explicit Gradient Learning From Augmented Data—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—0
Generalizable speech deepfake detection via meta-learned LoRA—0
A General framework for PAC-Bayes Bounds for Meta-Learning—0
Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling—0
Improved Compositional Generalization by Generating Demonstrations for Meta-Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Improved Few-Shot Visual Classification—0
Improved Generalization Risk Bounds for Meta-Learning with PAC-Bayes-kl Analysis—0
Improved Meta Learning for Low Resource Speech Recognition—0
Improved Meta-Learning Training for Speaker Verification—0
Knowledge-driven Meta-learning for CSI Feedback—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