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 1651–1675 of 3569 papers

TitleStatusHype
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
A Meta-Learning Algorithm for Interrogative Agendas—0
Task Weighting in Meta-learning with Trajectory Optimisation—0
Metalearning generalizable dynamics from trajectories—0
Tracking Brand-Associated Polarity-Bearing Topics in User ReviewsCode0
On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis—0
Bi-Level Meta-Learning for Few-Shot Domain Generalization—0
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan—0
s-Adaptive Decoupled Prototype for Few-Shot Object Detection—0
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation—0
Improving Generalization of Meta-Learning With Inverted Regularization at Inner-Level—0
A Simple Recipe to Meta-Learn Forward and Backward Transfer—0
Ground-Truth Free Meta-Learning for Deep Compressive Sampling—0
Global Perception Based Autoregressive Neural Processes—0
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction—0
An Erudite Fine-Grained Visual Classification Model—0
Eliminating Meta Optimization Through Self-Referential Meta Learning—0
Wormhole MAML: Meta-Learning in Glued Parameter Space—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer—0
Reusable Options through Gradient-based Meta LearningCode0
Robust Meta-Representation Learning via Global Label Inference and ClassificationCode0
LogAnMeta: Log Anomaly Detection Using Meta Learning—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
Robust and Resource-efficient Machine Learning Aided Viewport Prediction in Virtual Reality—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