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 2921–2930 of 3569 papers

TitleStatusHype
FHIST: A Benchmark for Few-shot Classification of Histological Images—0
FIND:Explainable Framework for Meta-learning—0
Finding online neural update rules by learning to remember—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling—0
Outlier detection using flexible categorisation and interrogative agendas—0
Flow to Learn: Flow Matching on Neural Network Parameters—0
Fodor and Pylyshyn's Legacy -- Still No Human-like Systematic Compositionality in Neural Networks—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