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 201225 of 3569 papers

TitleStatusHype
Learning Domain Invariant Prompt for Vision-Language ModelsCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Domain-General Crowd Counting in Unseen ScenariosCode1
Multi-Modal Few-Shot Temporal Action DetectionCode1
Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation ModelsCode1
Generalizable Implicit Neural Representations via Instance Pattern ComposersCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot LearningCode1
Toward Unsupervised Outlier Model SelectionCode1
A picture of the space of typical learnable tasksCode1
Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback PathwaysCode1
m^4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-AdapterCode1
Continued Pretraining for Better Zero- and Few-Shot PromptabilityCode1
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-RankingCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Few-shot Relational Reasoning via Connection Subgraph PretrainingCode1
Stock Trading Volume Prediction with Dual-Process Meta-LearningCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
Meta-Learning with Self-Improving Momentum TargetCode1
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-ExpertsCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Learning to Learn and Sample BRDFsCode1
Hypernetwork approach to Bayesian MAMLCode1
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-LearningCode1
MetaPrompting: Learning to Learn Better PromptsCode1
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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