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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 3041–3050 of 3687 papers

Title	Date	Tasks	Status	Hype
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy	Feb 19, 2021	Active LearningHierarchical Reinforcement Learning	CodeCode Available	0
Large Language Models for Ingredient Substitution in Food Recipes using Supervised Fine-tuning and Direct Preference Optimization	Dec 6, 2024	Multi-Task Learning	CodeCode Available	0
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers	Oct 13, 2022	Language ModelingLanguage Modelling	CodeCode Available	0
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network	Nov 8, 2021	Multi-Task LearningRelation	CodeCode Available	0
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning	Oct 25, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning	Oct 16, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation	Oct 7, 2023	Contrastive LearningMulti-Task Learning	CodeCode Available	0
Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data	Nov 7, 2023	Knowledge DistillationMulti-Task Learning	CodeCode Available	0
CALM: Consensus-Aware Localized Merging for Multi-Task Learning	Jun 16, 2025	Multi-Task LearningTask Arithmetic	CodeCode Available	0
Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?	Oct 1, 2018	Multi-Task Learningnamed-entity-recognition	CodeCode Available	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