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 1726–1750 of 3569 papers

TitleStatusHype
Meta Learning for Natural Language Processing: A Survey—0
Improving both domain robustness and domain adaptability in machine translation—0
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer—0
Towards Sample-efficient Overparameterized Meta-learningCode0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
Graph Representation Learning for Multi-Task Settings: a Meta-Learning ApproachCode1
Budget-aware Few-shot Learning via Graph Convolutional Network—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Deep Reinforcement Learning, a textbook—0
Learning To Learn by Jointly Optimizing Neural Architecture and Weights—0
Learning To Learn and Remember Super Long Multi-Domain Task SequenceCode1
Meta Distribution Alignment for Generalizable Person Re-IdentificationCode0
Towards Multi-Domain Single Image Dehazing via Test-Time Training—0
MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning—0
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship—0
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced DataCode0
Feature-context driven Federated Meta-Learning for Rare Disease Prediction—0
Recursive Least-Squares Estimator-Aided Online Learning for Visual TrackingCode1
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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