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 3126–3150 of 3569 papers

TitleStatusHype
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All—0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout—0
Learning to Learn to Compress—0
Labeled Memory Networks for Online Model Adaptation—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features—0
Large-Scale Meta-Learning with Continual Trajectory Shifting—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Layer-Wise Adaptive Updating for Few-Shot Image Classification—0
DECN: Evolution Inspired Deep Convolution Network for Black-box Optimization—0
LEAF: A Benchmark for Federated Settings—0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learning a Better Initialization for Soft Prompts via Meta-Learning—0
Learning Abstract Task Representations—0
Learning active learning at the crossroads? evaluation and discussion—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Learning Adaptive Loss for Robust Learning with Noisy Labels—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation—0
Learning Attentive Meta-Transfer—0
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning—0
Learning Differential Operators for Interpretable Time Series Modeling—0
Learning Effective Exploration Strategies For Contextual Bandits—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