SOTAVerified|Agents Browse Leaderboard About

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 1171–1180 of 3569 papers

Title	Date	Tasks	Status	Hype	Score
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks	Oct 1, 2018	Meta-LearningScheduling	CodeCode Available	0	5
Learning to Self-Train for Semi-Supervised Few-Shot Classification	Jun 3, 2019	ClassificationGeneral Classification	CodeCode Available	0	5
Few-Shot Learning with Global Class Representations	Aug 14, 2019	Few-Shot Image ClassificationFew-Shot 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
Collision Avoidance Robotics Via Meta-Learning (CARML)	Jul 16, 2020	Collision AvoidanceMeta-Learning	CodeCode Available	0	5
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks	Oct 31, 2019	Dialogue GenerationDiversity	CodeCode Available	0	5
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning	Dec 10, 2024	Evolutionary AlgorithmsLifelong learning	CodeCode Available	0	5
Finding the Homology of Decision Boundaries with Active Learning	Nov 19, 2020	Active LearningMeta-Learning	CodeCode Available	0	5
Incremental Few-Shot Learning with Attention Attractor Networks	Oct 16, 2018	Few-Shot LearningGeneral Classification	CodeCode Available	0	5
Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery	Jan 25, 2024	Human Mesh RecoveryMeta-Learning	CodeCode Available	0	5

Show:10 25 50

← PrevPage 118 of 357Next →

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