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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 801–810 of 3687 papers

Title	Date	Tasks	Status	Hype
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection	Oct 12, 2023	3D ReconstructionMulti-Task Learning	CodeCode Available	0
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning	Oct 11, 2023	Decision MakingImitation Learning	—Unverified	0
Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance	Oct 11, 2023	Depth EstimationMulti-Task Learning	—Unverified	0
Denoising Task Routing for Diffusion Models	Oct 11, 2023	DenoisingMulti-Task Learning	CodeCode Available	1
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning	Oct 11, 2023	Common Sense ReasoningContrastive Learning	—Unverified	0
VSANet: Real-time Speech Enhancement Based on Voice Activity Detection and Causal Spatial Attention	Oct 11, 2023	Action DetectionActivity Detection	—Unverified	0
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach	Oct 10, 2023	DecoderMulti-Task Learning	—Unverified	0
M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images	Oct 9, 2023	DecoderMedical Image Segmentation	—Unverified	0
Boosting Facial Action Unit Detection Through Jointly Learning Facial Landmark Detection and Domain Separation and Reconstruction	Oct 8, 2023	Action Unit DetectionContrastive Learning	—Unverified	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

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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