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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 3151–3160 of 3687 papers

Title	Date	Tasks	Status	Hype
Multi-task Planar Reconstruction with Feature Warping Guidance	Nov 25, 2023	3D ReconstructionInstance Segmentation	CodeCode Available	0
Vulnerability-Aware Spatio-Temporal Learning for Generalizable and Interpretable Deepfake Video Detection	Jan 2, 2025	Face SwappingMulti-Task Learning	CodeCode Available	0
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception	Jan 20, 2018	Autonomous DrivingMulti-Task Learning	CodeCode Available	0
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms	Sep 11, 2019	Multi-class ClassificationMulti-Label Learning	CodeCode Available	0
Learning visual-based deformable object rearrangement with local graph neural networks	Oct 16, 2023	Graph Neural NetworkMulti-Task Learning	CodeCode Available	0
Multi-task problems are not multi-objective	Oct 14, 2021	BIG-bench Machine LearningMulti-Task Learning	CodeCode Available	0
Bidirectional Domain Adaptation Using Weighted Multi-Task Learning	Aug 1, 2021	Domain AdaptationMulti-Task Learning	CodeCode Available	0
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection	Aug 17, 2023	Anomaly DetectionMixture-of-Experts	CodeCode Available	0
Learning Zero-Shot Multifaceted Visually Grounded Word Embeddings via Multi-Task Training	Apr 15, 2021	Multi-Task LearningWord Embeddings	CodeCode Available	0
End-to-end Multimodal Emotion and Gender Recognition with Dynamic Joint Loss Weights	Sep 4, 2018	Multi-Task Learning	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