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 2101–2125 of 3569 papers

TitleStatusHype
Scalable Meta-Learning with Gaussian Processes—0
Scaling Opponent Shaping to High Dimensional Games—0
Scarce Data Driven Deep Learning of Drones via Generalized Data Distribution Space—0
Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach—0
Search-based Optimisation of LLM Learning Shots for Story Point Estimation—0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Seeded self-play for language learning—0
Seeker based Adaptive Guidance via Reinforcement Meta-Learning Applied to Asteroid Close Proximity Operations—0
SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement—0
Selective classification using a robust meta-learning approach—0
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction—0
Self-Configuring nnU-Nets Detect Clouds in Satellite Images—0
Self-Domain Adaptation for Face Anti-Spoofing—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Self-Supervised Deep Visual Odometry with Online Adaptation—0
Self-Supervised Fast Adaptation for Denoising via Meta-Learning—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath—0
Self-Supervised Tuning for Few-Shot Segmentation—0
Self-Supervised Video Representation Learning with Meta-Contrastive Network—0
Self-tuning hyper-parameters for unsupervised cross-lingual tokenization—0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings—0
Semantic Inference Network for Few-shot Streaming Label Learning—0
Semi Few-Shot Attribute Translation—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