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 1011–1020 of 3569 papers

TitleStatusHype
Black-box Prompt Tuning with Subspace Learning—0
Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI—0
Generative Meta-Learning for Zero-Shot Relation Triplet Extraction—0
Accelerating Neural Self-Improvement via Bootstrapping—0
Model-agnostic Measure of Generalization DifficultyCode0
META-SMGO-Δ: similarity as a prior in black-box optimization—0
Analogy-Forming Transformers for Few-Shot 3D Parsing—0
On the Generalization Error of Meta Learning for the Gibbs Algorithm—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Combining Adversaries with Anti-adversaries in Training—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