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 1601–1625 of 3569 papers

TitleStatusHype
Instance-Conditional Timescales of Decay for Non-Stationary Learning—0
Learning Prototype-oriented Set Representations for Meta-Learning—0
Learning to Unlearn for Robust Machine Unlearning—0
Learning Soft Labels via Meta Learning—0
Learning State-Dependent Losses for Inverse Dynamics Learning—0
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
Learning Tensor Representations for Meta-Learning—0
Connecting Context-specific Adaptation in Humans to Meta-learning—0
Learning to Actively Learn: A Robust Approach—0
Context-Conditioned Spatio-Temporal Predictive Learning for Reliable V2V Channel Prediction—0
A Few Shot Adaptation of Visual Navigation Skills to New Observations using Meta-Learning—0
Learning to Adapt Multi-View Stereo by Self-Supervision—0
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection—0
From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding—0
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model—0
From Biased Data to Unbiased Models: a Meta-Learning Approach—0
Learning to Adapt to Semantic Shift—0
From Abstraction to Reality: DARPA's Vision for Robust Sim-to-Real Autonomy—0
Context-aware Visual Tracking with Joint Meta-updating—0
Learning to Augment via Implicit Differentiation for Domain Generalization—0
Auto-Meta: Automated Gradient Based Meta Learner Search—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
Learning to Segment Skin Lesions from Noisy Annotations—0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments—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