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 2961–2970 of 3569 papers

TitleStatusHype
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss—0
Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Generalized Visual Quality Assessment of GAN-Generated Face Images—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Generating meta-learning tasks to evolve parametric loss for classification learning—0
Generating Personalized Dialogue via Multi-Task Meta-Learning—0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—0
Generative Conversational 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