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 2226–2250 of 3569 papers

TitleStatusHype
Task-Aware Part Mining Network for Few-Shot Learning—0
Task Calibration for Distributional Uncertainty in Few-Shot Classification—0
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation—0
Task-Distributionally Robust Data-Free Meta-Learning—0
Task-driven Image Fusion with Learnable Fusion Loss—0
Task Fingerprinting for Meta Learning in Biomedical Image Analysis—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Task Relatedness-Based Generalization Bounds for Meta Learning—0
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
Task Weighting in Meta-learning with Trajectory Optimisation—0
TalaGen: A System for Automatic Tala Identification and Generation—0
Teaching Models to Improve on Tape—0
Teaching to Learn: Sequential Teaching of Agents with Inner States—0
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection—0
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks—0
Temporal Variational Implicit Neural Representations—0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Test-Time Adaptation for Generalizable Task Progress Estimation—0
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning—0
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach—0
Thalamocortical contribution to solving credit assignment in neural systems—0
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning—0
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems—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