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

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Showing 1881–1890 of 3569 papers

TitleStatusHype
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
One-Shot Imitation Learning—0
One to Many: Adaptive Instrument Segmentation via Meta Learning and Dynamic Online Adaptation in Robotic Surgical Video—0
On Extending Neural Networks with Loss Ensembles for Text Classification—0
On Hard Episodes in Meta-Learning—0
On Implicit Bias in Overparameterized Bilevel Optimization—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Online Adaptation through Meta-Learning for Stereo Depth Estimation—0
Online Algorithms for Hierarchical Inference in Deep Learning applications at the Edge—0
Online Bayesian Meta-Learning for Cognitive Tracking Radar—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