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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 2981–2990 of 3687 papers

Title	Date	Tasks	Status	Hype
Weakly Supervised Image Classification Through Noise Regularization	Jun 1, 2019	ClassificationGeneral Classification	—Unverified	0
Does Learning Specific Features for Related Parts Help Human Pose Estimation?	Jun 1, 2019	Multi-Task LearningPose Estimation	—Unverified	0
Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations	Jun 1, 2019	Multi-Task LearningNovel Object Detection	—Unverified	0
Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach	Jun 1, 2019	Autonomous DrivingDepth Completion	CodeCode Available	0
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation	Jun 1, 2019	Image-level Supervised Instance SegmentationMulti-Task Learning	CodeCode Available	0
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains	May 31, 2019	Multi-Task Learning	CodeCode Available	0
Non-linear Multitask Learning with Deep Gaussian Processes	May 29, 2019	BenchmarkingGaussian Processes	—Unverified	0
Gaining Extra Supervision via Multi-task learning for Multi-Modal Video Question Answering	May 28, 2019	Inductive BiasMetric Learning	—Unverified	0
DSReg: Using Distant Supervision as a Regularizer	May 28, 2019	Multi-Task LearningReading Comprehension	—Unverified	0
Leveraging Medical Visual Question Answering with Supporting Facts	May 28, 2019	DiversityMedical Visual Question Answering	—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