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 101125 of 3569 papers

TitleStatusHype
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal PerspectiveCode1
MotherNet: Fast Training and Inference via Hyper-Network TransformersCode1
Concrete Subspace Learning based Interference Elimination for Multi-task Model FusionCode1
Meta ControlNet: Enhancing Task Adaptation via Meta LearningCode1
Automating Continual LearningCode1
SEGIC: Unleashing the Emergent Correspondence for In-Context SegmentationCode1
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty PredictionCode1
MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learningCode1
Massive Editing for Large Language Models via Meta LearningCode1
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning ApplicationsCode1
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUsCode1
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-EncoderCode1
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation ExtractionCode1
Implicit meta-learning may lead language models to trust more reliable sourcesCode1
Group Preference Optimization: Few-Shot Alignment of Large Language ModelsCode1
Context-Aware Meta-LearningCode1
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot ClassificationCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Learn From Model Beyond Fine-Tuning: A SurveyCode1
Self-Supervised Dataset Distillation for Transfer LearningCode1
Neural Relational Inference with Fast Modular Meta-learningCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
Fine-grained Recognition with Learnable Semantic Data AugmentationCode1
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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