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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 1181–1190 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Fine-Grained Trajectory-based Travel Time Estimation for Multi-city Scenarios Based on Deep Meta-Learning	Jan 20, 2022	DecoderMeta-Learning	CodeCode Available	0	5
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data	Jul 28, 2018	Fine-Grained Visual CategorizationMeta-Learning	CodeCode Available	0	5
Joint inference and input optimization in equilibrium networks	Nov 25, 2021	DenoisingMeta-Learning	CodeCode Available	0	5
Adversarial Attacks on Graph Neural Networks via Meta Learning	Feb 22, 2019	General ClassificationMeta-Learning	CodeCode Available	0	5
Few-Shot Learning with Global Class Representations	Aug 14, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Interpretable Meta-Measure for Model Performance	Jun 2, 2020	Meta-Learningmodel	CodeCode Available	0	5
Interval Bound Interpolation for Few-shot Learning with Few Tasks	Apr 7, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Inverse Learning with Extremely Sparse Feedback for Recommendation	Nov 14, 2023	Meta-Learning	CodeCode Available	0	5
Collision Avoidance Robotics Via Meta-Learning (CARML)	Jul 16, 2020	Collision AvoidanceMeta-Learning	CodeCode Available	0	5
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing	Aug 11, 2023	CPUGPU	CodeCode Available	0	5

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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