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 501525 of 3569 papers

TitleStatusHype
Overcoming Data Limitation in Medical Visual Question AnsweringCode1
PACOH: Bayes-Optimal Meta-Learning with PAC-GuaranteesCode1
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient OptimizationCode1
PersonalLLM: Tailoring LLMs to Individual PreferencesCode1
Pin the Memory: Learning to Generalize Semantic SegmentationCode1
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person RetrievalCode1
Camera Distortion-aware 3D Human Pose Estimation in Video with Optimization-based Meta-LearningCode1
An Analysis of the Adaptation Speed of Causal ModelsCode1
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer LearningCode1
Pre-training to Match for Unified Low-shot Relation ExtractionCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Discovering modular solutions that generalize compositionallyCode1
CD-FSOD: A Benchmark for Cross-domain Few-shot Object DetectionCode1
DIP: Unsupervised Dense In-Context Post-training of Visual RepresentationsCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the WildCode1
Diffusion-Based Neural Network Weights GenerationCode1
Direct Differentiable Augmentation SearchCode1
Discovering Reinforcement Learning AlgorithmsCode1
An Enhanced Span-based Decomposition Method for Few-Shot Sequence LabelingCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Delving Deep Into Many-to-Many Attention for Few-Shot Video Object SegmentationCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Deep Random Projector: Accelerated Deep Image PriorCode1
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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