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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 701–710 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation	Apr 25, 2023	Deep Reinforcement LearningMulti-Task Learning	CodeCode Available	0	5
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning	Jan 28, 2020	DecoderGoal-Oriented Dialog	CodeCode Available	0	5
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-Tuning	Aug 1, 2021	DenoisingFew-Shot Learning	CodeCode Available	0	5
Augmenting Biomedical Named Entity Recognition with General-domain Resources	Jun 15, 2024	Language ModellingMulti-Task Learning	CodeCode Available	0	5
Improving Sentiment Analysis with Multi-task Learning of Negation	Jun 18, 2019	Multi-Task LearningNegation	CodeCode Available	0	5
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning	May 24, 2023	Continual Learningimage-classification	CodeCode Available	0	5
Incomplete Utterance Rewriting with Editing Operation Guidance and Utterance Augmentation	Mar 20, 2025	Multi-Task Learning	CodeCode Available	0	5
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome	Aug 7, 2018	Missing ValuesMulti-Task Learning	CodeCode Available	0	5
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning	Oct 16, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	0	5
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing	Jan 1, 2021	Multi-Task Learning	CodeCode Available	0	5

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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