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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 1141–1150 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Interval Bound Interpolation for Few-shot Learning with Few Tasks	Apr 7, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	0	5
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach	Jul 21, 2020	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	0	5
Inverse Learning with Extremely Sparse Feedback for Recommendation	Nov 14, 2023	Meta-Learning	CodeCode Available	0	5
Interpretable Meta-Measure for Model Performance	Jun 2, 2020	Meta-Learningmodel	CodeCode Available	0	5
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups	Nov 3, 2024	Binary ClassificationComplex Word Identification	CodeCode Available	0	5
It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation	Sep 30, 2023	Density EstimationMeta-Learning	CodeCode Available	0	5
Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition	Nov 9, 2021	Continual LearningKnowledge Distillation	CodeCode Available	0	5
A Partially Supervised Reinforcement Learning Framework for Visual Active Search	Oct 15, 2023	Deep Reinforcement LearningMeta-Learning	CodeCode Available	0	5
Few-shot Quality-Diversity Optimization	Sep 14, 2021	DiversityMeta-Learning	CodeCode Available	0	5
Incremental Few-Shot Learning with Attention Attractor Networks	Oct 16, 2018	Few-Shot LearningGeneral Classification	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