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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 1031–1040 of 3687 papers

Title	Date	Tasks	Status	Hype
UCF: Uncovering Common Features for Generalizable Deepfake Detection	Apr 27, 2023	Binary ClassificationDecoder	CodeCode Available	3
π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation	Apr 27, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	1
Multi-Task Learning Regression via Convex Clustering	Apr 26, 2023	ClusteringMulti-Task Learning	—Unverified	0
Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing	Apr 25, 2023	MarketingMulti-Task Learning	—Unverified	0
Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles	Apr 25, 2023	Autonomous VehiclesIntent Detection	—Unverified	0
Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports	Apr 25, 2023	Multi-Task LearningNatural Language Inference	CodeCode Available	0
A Multi-Task Approach to Robust Deep Reinforcement Learning for Resource Allocation	Apr 25, 2023	Deep Reinforcement LearningMulti-Task Learning	CodeCode Available	0
Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion	Apr 25, 2023	Collision AvoidanceDecision Making	—Unverified	0
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture	Apr 25, 2023	Autonomous DrivingMulti-Task Learning	—Unverified	0
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge	Apr 21, 2023	Multi-Task LearningRobotic Grasping	—Unverified	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