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 1051–1075 of 3569 papers

TitleStatusHype
DoE2Vec: Deep-learning Based Features for Exploratory Landscape AnalysisCode1
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
Scalable Bayesian Meta-Learning through Generalized Implicit GradientsCode0
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization—0
Towards Unbiased Calibration using Meta-Regularization—0
GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEsCode1
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
Autoregressive Conditional Neural ProcessesCode0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization—0
A Generalised Deep Meta-Learning Model for Automated Quality Control of Cardiovascular Magnetic Resonance ImagesCode0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence—0
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
Assessor-Guided Learning for Continual EnvironmentsCode0
Towards Reliable Neural Machine Translation with Consistency-Aware Meta-Learning—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-LearningCode1
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot LearningCode0
Arbitrary Order Meta-Learning with Simple Population-Based Evolution—0
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI—0
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation—0
Meta-learning approaches for few-shot learning: A survey of recent advances—0
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer AdaptersCode0
Learning Distortion Invariant Representation for Image Restoration from A Causality PerspectiveCode1
Show:102550
← PrevPage 43 of 143Next →
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