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 1761–1770 of 3569 papers

TitleStatusHype
MVDG: A Unified Multi-view Framework for Domain GeneralizationCode0
Dual Path Structural Contrastive Embeddings for Learning Novel Objects—0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning—0
Meta Propagation Networks for Graph Few-shot Semi-supervised LearningCode1
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional NetworksCode1
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learningCode0
Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker—0
Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning—0
Automatic tuning of hyper-parameters of reinforcement learning algorithms using Bayesian optimization with behavioral cloning—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