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 1901–1925 of 3569 papers

TitleStatusHype
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
Across-Task Neural Architecture Search via Meta Learning—0
Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty—0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks—0
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification—0
3D Meta-Segmentation Neural Network—0
Graph Meta Network for Multi-Behavior RecommendationCode1
Meta-Learning with Task-Adaptive Loss Function for Few-Shot LearningCode1
Meta-Learning 3D Shape Segmentation Functions—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning—0
Influence-Balanced Loss for Imbalanced Visual ClassificationCode1
Meta Internal LearningCode1
Online Hyperparameter Meta-Learning with Hypergradient Distillation—0
MetaPix: Domain Transfer for Semantic Segmentation by Meta Pixel WeightingCode0
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming EventsCode0
Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide SusceptibilityCode1
An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset—0
Home Appliance Review Research Via Adversarial Reptile—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