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 971–980 of 3569 papers

TitleStatusHype
Boosting Generalizability towards Zero-Shot Cross-Dataset Single-Image Indoor Depth by Meta-Initialization—0
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification—0
Explaining the Performance of Multi-label Classification Methods with Data Set Properties—0
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning—0
Automatic low-bit hybrid quantization of neural networks through meta learning—0
Distributed Evolution Strategies Using TPUs for Meta-Learning—0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
Distributed Estimation by Two Agents with Different Feature Spaces—0
Automatic Learning to Detect Concept Drift—0
Amazon SageMaker Autopilot: a white box AutoML solution at scale—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