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

Showing 16261650 of 3569 papers

TitleStatusHype
Temporal Transductive Inference for Few-Shot Video Object SegmentationCode0
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison0
Learn to Adapt for Monocular Depth Estimation0
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification0
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel DecompositionCode1
Pre-training to Match for Unified Low-shot Relation ExtractionCode1
Multidimensional Belief Quantification for Label-Efficient Meta-LearningCode0
Domain-Generalized Textured Surface Anomaly Detection0
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation0
Meta-X_NLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and GenerationCode0
Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-LearningCode0
Meta-Learning for Online Update of Recommender SystemsCode0
Negative Inner-Loop Learning Rates Learn Universal Features0
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach0
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications0
Practical Conditional Neural Processes Via Tractable Dependent Predictions0
Learning What Not to Segment: A New Perspective on Few-Shot SegmentationCode2
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels0
Deep Transfer Learning with Graph Neural Network for Sensor-Based Human Activity Recognition0
MetAug: Contrastive Learning via Meta Feature AugmentationCode1
SuperCone: Unified User Segmentation over Heterogeneous Experts via Concept Meta-learning0
What Matters For Meta-Learning Vision Regression Tasks?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8Unverified
2MZMeta-train success rate97.6Unverified
3MAMLMeta-test success rate36Unverified
4RL^2Meta-test success rate10Unverified
5DnCMeta-test success rate5.4Unverified
6PEARLMeta-test success rate0Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60Unverified
2Multi-task multi-head SACAverage Success Rate35.85Unverified
3DisCorAverage Success Rate26Unverified
4NDPAverage Success Rate11Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5Unverified
2MZMeta-test success rate (zero-shot)17.7Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75Unverified