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 2121–2130 of 3569 papers

TitleStatusHype
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations—0
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship—0
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced DataCode0
Feature-context driven Federated Meta-Learning for Rare Disease Prediction—0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains—0
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation—0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Dynamic Channel Access via Meta-Reinforcement 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