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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1871–1880 of 3687 papers

Title	Date	Tasks	Status	Hype
Zero-Shot Dependency Parsing with Worst-Case Aware Automated Curriculum Learning	Mar 16, 2022	Cross-Lingual TransferDependency Parsing	CodeCode Available	0
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering	Mar 14, 2022	DecoderDomain Generalization	—Unverified	0
A Tree-Structured Multi-Task Model Recommender	Mar 10, 2022	modelMulti-Task Learning	CodeCode Available	0
A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation	Mar 9, 2022	Depth EstimationGenerative Adversarial Network	—Unverified	0
A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network	Mar 9, 2022	DeblurringMulti-Task Learning	—Unverified	0
Sequential Multi-task Learning with Task Dependency for Appeal Judgment Prediction	Mar 9, 2022	ArticlesMulti-Task Learning	—Unverified	0
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations	Mar 8, 2022	Data Augmentationdomain classification	—Unverified	0
YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers	Mar 8, 2022	Multi-Task LearningQuantization	—Unverified	0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks	Mar 8, 2022	Language ModelingLanguage Modelling	—Unverified	0
SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding	Mar 7, 2022	Language ModellingMasked Language Modeling	—Unverified	0

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All datasets QM9 Cityscapes test NYUv2 OMNIGLOT CelebA UTKFace wireframe dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	PCGrad	∆m%	125.7	—	Unverified
2	CAGrad	∆m%	112.8	—	Unverified
3	IMTL-G	∆m%	77.2	—	Unverified
4	Nash-MTL	∆m%	62	—	Unverified
5	BayesAgg-MTL	∆m%	53.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	mIoU	76.41	—	Unverified
2	Nash-MTL	mIoU	75.41	—	Unverified
3	MultiObjectiveOptimization	mIoU	66.63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SwinMTL	Mean IoU	58.14	—	Unverified
2	Nash-MTL	Mean IoU	40.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Gumbel-Matrix Routing	Average Accuracy	93.52	—	Unverified
2	Mixture-of-Experts	Average Accuracy	92.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MGDA-UB	Error	8.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BayesAgg-MTL	delta_m	-2.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LETR	FH	83.3	—	Unverified