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 1851–1875 of 3569 papers

TitleStatusHype
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
Augmentation Learning for Semi-Supervised Classification—0
Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-LearningCode0
The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning—0
PointFix: Learning to Fix Domain Bias for Robust Online Stereo AdaptationCode0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning—0
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations—0
Can we achieve robustness from data alone?—0
Meta Spatio-Temporal Debiasing for Video Scene Graph Generation—0
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration—0
MetaComp: Learning to Adapt for Online Depth Completion—0
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Riemannian Stochastic Gradient Method for Nested Composition Optimization—0
On the cross-lingual transferability of multilingual prototypical models across NLU tasks—0
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution—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