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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 1751–1760 of 3569 papers

Title	Date	Tasks	Status	Hype
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation	Dec 27, 2021	DisentanglementDomain Adaptation	—Unverified	0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?	Dec 27, 2021	ArticlesMedical Visual Question Answering	—Unverified	0
MetaCVR: Conversion Rate Prediction via Meta Learning in Small-Scale Recommendation Scenarios	Dec 27, 2021	Meta-Learning	—Unverified	0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains	Dec 27, 2021	ClassificationFew-Shot Learning	—Unverified	0
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning	Dec 25, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	1
Dynamic Channel Access via Meta-Reinforcement Learning	Dec 24, 2021	channel selectionDeep Reinforcement Learning	—Unverified	0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence	Dec 24, 2021	DiversityFew-Shot Learning	—Unverified	0
Does MAML Only Work via Feature Re-use? A Data Centric Perspective	Dec 24, 2021	Few-Shot LearningMeta-Learning	CodeCode Available	0
Deep Neuroevolution Squeezes More out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification	Dec 24, 2021	Deep Reinforcement LearningMeta-Learning	—Unverified	0
Graph Few-shot Class-incremental Learning	Dec 23, 2021	class-incremental learningClass Incremental Learning	CodeCode Available	1

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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