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 1961–1970 of 3569 papers

TitleStatusHype
Part 1: Training Sets & ASG Transforms—0
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition—0
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning—0
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment—0
Partner-Assisted Learning for Few-Shot Image Classification—0
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition—0
PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling—0
Performance-Weighed Policy Sampling for Meta-Reinforcement Learning—0
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering—0
PersA-FL: Personalized Asynchronous Federated 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