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 1851–1900 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
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
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot LearningCode0
problexity -- an open-source Python library for binary classification problem complexity assessment—0
Effective Few-Shot Named Entity Linking by Meta-LearningCode0
MetaAge: Meta-Learning Personalized Age EstimatorsCode0
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs—0
Continual Few-Shot Learning with Adversarial Class Storage—0
Few 'Zero Level Set'-Shot Learning of Shape Signed Distance Functions in Feature SpaceCode0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—0
MACFE: A Meta-learning and Causality Based Feature Engineering FrameworkCode0
On the Subspace Structure of Gradient-Based Meta-Learning—0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data—0
Meta-Learning the Difference: Preparing Large Language Models for Efficient AdaptationCode0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
Online Bayesian Meta-Learning for Cognitive Tracking Radar—0
PAC Prediction Sets for Meta-Learning—0
FewSOL: A Dataset for Few-Shot Object Learning in Robotic EnvironmentsCode0
A Unified Meta-Learning Framework for Dynamic Transfer LearningCode0
Betty: An Automatic Differentiation Library for Multilevel Optimization—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Adaptive Fine-Grained Sketch-Based Image RetrievalCode0
The least-control principle for local learning at equilibriumCode0
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—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