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

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Showing 1401–1450 of 3569 papers

TitleStatusHype
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification—0
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
Visual Localization via Few-Shot Scene Region ClassificationCode1
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects—0
GEDI: A Graph-based End-to-end Data Imputation Framework—0
Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation—0
On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine LearningCode0
Intelligent MIMO Detection Using Meta Learning—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Learning to Generalize with Object-centric Agents in the Open World Survival Game CrafterCode1
SA-NET.v2: Real-time vehicle detection from oblique UAV images with use of uncertainty estimation in deep meta-learning—0
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Transformers as Meta-Learners for Implicit Neural RepresentationsCode1
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Augmentation Learning for Semi-Supervised Classification—0
Centroids Matching: an efficient Continual Learning approach operating in the embedding spaceCode0
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
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation ExploitationCode2
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Meta-Learning based Degradation Representation for Blind Super-ResolutionCode1
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—0
PointFix: Learning to Fix Domain Bias for Robust Online Stereo AdaptationCode0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations—0
Localization of Coordinated Cyber-Physical Attacks in Power Grids Using Moving Target Defense and Deep Learning—0
ArtFID: Quantitative Evaluation of Neural Style TransferCode1
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification—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
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Tackling Long-Tailed Category Distribution Under Domain ShiftsCode1
Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing—0
Test-Time Adaptation via Conjugate Pseudo-labelsCode1
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
Multi-Task and Transfer Learning for Federated Learning Applications—0
Meta-Referential Games to Learn Compositional Learning BehavioursCode0
A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction—0
problexity -- an open-source Python library for binary classification problem complexity assessment—0
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot LearningCode0
Learning Deep Time-index Models for Time Series ForecastingCode2
MetaAge: Meta-Learning Personalized Age EstimatorsCode0
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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