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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 1621–1630 of 3687 papers

Title	Date	Tasks	Status	Hype
Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers	Oct 12, 2022	Multi-Task Learning	—Unverified	0
Understanding Impacts of Task Similarity on Backdoor Attack and Detection	Oct 12, 2022	Backdoor AttackMulti-Task Learning	—Unverified	0
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding	Oct 12, 2022	Multi-Task LearningTransfer Learning	CodeCode Available	0
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency	Oct 11, 2022	Autonomous DrivingEdge Detection	CodeCode Available	0
Visually Similar Products Retrieval for Shopsy	Oct 10, 2022	AttributeImage Compression	—Unverified	0
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks	Oct 9, 2022	DecoderImage Segmentation	CodeCode Available	0
Multi-Task Dynamical Systems	Oct 8, 2022	Multi-Task LearningTime Series	CodeCode Available	0
Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task	Oct 8, 2022	Multi-Task LearningTranslation	CodeCode Available	0
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models	Oct 8, 2022	GPUMulti-Task Learning	—Unverified	0
UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation	Oct 7, 2022	Binary ClassificationData Augmentation	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