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 2276–2300 of 3569 papers

TitleStatusHype
TinyReptile: TinyML with Federated Meta-Learning—0
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions—0
TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning—0
Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks—0
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML—0
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies—0
Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling—0
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples—0
Toward Multimodal Model-Agnostic Meta-Learning—0
Towards 3D Semantic Scene Completion for Autonomous Driving: A Meta-Learning Framework Empowered by Deformable Large-Kernel Attention and Mamba Model—0
Towards an Unsupervised Method for Model Selection in Few-Shot Learning—0
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Automated Error Analysis: Learning to Characterize Errors—0
Towards Better Meta-Initialization with Task Augmentation for Kindergarten-aged Speech Recognition—0
Towards explainable meta-learning—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Discriminative Representation with Meta-learning for Colonoscopic Polyp Re-Identification—0
Towards Efficient and Effective Alignment of Large Language Models—0
Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks—0
Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Experts—0
Towards General and Efficient Online Tuning for Spark—0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation—0
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning—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