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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 951–960 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Hybrid Losses for Hierarchical Embedding Learning	Jan 22, 2025	Multi-Task LearningRetrieval	CodeCode Available	0	5
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse	Oct 3, 2023	feature selectionimage-classification	CodeCode Available	0	5
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes	Apr 4, 2024	In-Context LearningMulti-Task Learning	CodeCode Available	0	5
On the Automatic Generation of Medical Imaging Reports	Nov 22, 2017	Medical Report GenerationMulti-Task Learning	CodeCode Available	0	5
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization	May 13, 2024	Multi-Task Learning	CodeCode Available	0	5
A Late-Stage Bitemporal Feature Fusion Network for Semantic Change Detection	Jun 15, 2024	Change DetectionEarth Observation	CodeCode Available	0	5
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge Distillation	Dec 24, 2024	Computational EfficiencyHandwritten Text Recognition	CodeCode Available	0	5
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling	Apr 30, 2021	ClassificationGeneral Classification	CodeCode Available	0	5
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation	Aug 14, 2019	Image SegmentationMedical Image Segmentation	CodeCode Available	0	5
Convex Learning of Multiple Tasks and their Structure	Apr 13, 2015	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