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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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 3331–3340 of 3569 papers

Title	Date	Tasks	Status	Hype
Online Adaptation through Meta-Learning for Stereo Depth Estimation	Apr 17, 2019	Depth EstimationMeta-Learning	—Unverified	0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning	Apr 17, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0
ACE: Adapting to Changing Environments for Semantic Segmentation	Apr 12, 2019	Meta-LearningSemantic Segmentation	—Unverified	0
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning	Apr 11, 2019	AttributeFew-Shot Learning	CodeCode Available	0
MxML: Mixture of Meta-Learners for Few-Shot Classification	Apr 11, 2019	ClassificationGeneral Classification	—Unverified	0
Few-Shot Learning with Localization in Realistic Settings	Apr 9, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout	Apr 8, 2019	Few-Shot LearningMeta-Learning	—Unverified	0
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning	Apr 7, 2019	General ClassificationMeta-Learning	CodeCode Available	0
Actively Seeking and Learning from Live Data	Apr 5, 2019	Domain AdaptationMeta-Learning	—Unverified	0
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization	Apr 4, 2019	Bayesian OptimizationGaussian Processes	CodeCode Available	0

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All datasets ML10 MT50 ML45 OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-train success rate	97.8	—	Unverified
2	MZ	Meta-train success rate	97.6	—	Unverified
3	MAML	Meta-test success rate	36	—	Unverified
4	RL^2	Meta-test success rate	10	—	Unverified
5	DnC	Meta-test success rate	5.4	—	Unverified
6	PEARL	Meta-test success rate	0	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SoftModule	Average Success Rate	60	—	Unverified
2	Multi-task multi-head SAC	Average Success Rate	35.85	—	Unverified
3	DisCor	Average Success Rate	26	—	Unverified
4	NDP	Average Success Rate	11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MZ+Recon	Meta-test success rate (zero-shot)	18.5	—	Unverified
2	MZ	Meta-test success rate (zero-shot)	17.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Metadrop	% Test Accuracy	95.75	—	Unverified