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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 2201–2210 of 3687 papers

Title	Date	Tasks	Status	Hype
Seed Stocking Via Multi-Task Learning	Jan 12, 2021	Multi-Task Learning	—Unverified	0
DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks	Jan 11, 2021	LemmatizationMulti-Task Learning	CodeCode Available	0
Explain and Predict, and then Predict Again	Jan 11, 2021	Explanation GenerationFact Verification	CodeCode Available	1
Transfer Learning and Augmentation for Word Sense Disambiguation	Jan 10, 2021	Data AugmentationMulti-Task Learning	—Unverified	0
Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: A Multi-task Learning Approach	Jan 10, 2021	feature selectionMulti-Task Learning	—Unverified	0
Spatial Object Recommendation with Hints: When Spatial Granularity Matters	Jan 8, 2021	AttributeMulti-Task Learning	—Unverified	0
Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario	Jan 7, 2021	Multi-Task LearningSpeaker Identification	CodeCode Available	0
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks	Jan 7, 2021	General ClassificationImage Classification	CodeCode Available	0
Line Segment Detection Using Transformers without Edges	Jan 6, 2021	DecoderLine Segment Detection	CodeCode Available	1
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing	Jan 5, 2021	Dependency ParsingLanguage Modeling	CodeCode Available	1

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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