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 1151–1200 of 3569 papers

TitleStatusHype
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Few-shot Node Classification with Extremely Weak SupervisionCode0
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Learning Generalized Zero-Shot Learners for Open-Domain Image GeolocalizationCode0
Learning Fast Adaptation with Meta Strategy OptimizationCode0
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote SensingCode0
Learning Deep Morphological Networks with Neural Architecture SearchCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning One-Shot Imitation from Humans without HumansCode0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
Learning advisor networks for noisy image classificationCode0
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained LearningCode0
Anti-Retroactive Interference for Lifelong LearningCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Leaping Through Time with Gradient-based Adaptation for RecommendationCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Learning an Explicit Hyperparameter Prediction Function Conditioned on TasksCode0
Learning to Demodulate from Few Pilots via Offline and Online Meta-LearningCode0
Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionCode0
Latent Bottlenecked Attentive Neural ProcessesCode0
Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic MaterialCode0
Learning to Self-Train for Semi-Supervised Few-Shot ClassificationCode0
Latent Task-Specific Graph Network SimulatorsCode0
FIGR: Few-shot Image Generation with ReptileCode0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Adversarial Attacks on Graph Neural Networks via Meta LearningCode0
Finding the Homology of Decision Boundaries with Active LearningCode0
Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in SegmentationCode0
LabelCraft: Empowering Short Video Recommendations with Automated Label CraftingCode0
Layer-compensated Pruning for Resource-constrained Convolutional Neural NetworksCode0
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary DataCode0
Few-Shot Learning with Global Class RepresentationsCode0
Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?Code0
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density EstimationCode0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
Consistency of Compositional Generalization across Multiple LevelsCode0
Inverse Learning with Extremely Sparse Feedback for RecommendationCode0
Interval Bound Interpolation for Few-shot Learning with Few TasksCode0
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain SetupsCode0
Learning to Learn without Forgetting using AttentionCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
Bayesian Active Meta-Learning for Few Pilot Demodulation and EqualizationCode0
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation ProcessingCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex ProgrammingCode0
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image RecognitionCode0
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary EnvironmentCode0
An Investigation of the Bias-Variance Tradeoff in Meta-GradientsCode0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
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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