SOTAVerified|Agents Browse Leaderboard About

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 391–400 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Multi-Task Deep Learning Framework for Building Footprint Segmentation	Apr 19, 2021	Boundary DetectionDeep Learning	CodeCode Available	1	5
End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent	Apr 12, 2022	Autonomous DrivingMulti-Task Learning	CodeCode Available	1	5
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis	Apr 4, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1	5
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees	Jan 17, 2022	Multi-Task Learning	CodeCode Available	1	5
Does Text Attract Attention on E-Commerce Images: A Novel Saliency Prediction Dataset and Method	Jan 1, 2022	Multi-Task LearningSaliency Prediction	CodeCode Available	1	5
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning	Oct 12, 2024	Domain AdaptationMulti-Task Learning	CodeCode Available	1	5
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models	Jul 14, 2020	DiversityMulti-Task Learning	CodeCode Available	1	5
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning	Oct 25, 2021	Multi-Task Learning	CodeCode Available	1	5
A Multi-task Framework for Infrared Small Target Detection and Segmentation	Jun 14, 2022	Multi-Task Learningobject-detection	CodeCode Available	1	5
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks	Nov 10, 2023	DiversityMulti-Task Learning	CodeCode Available	1	5

Show:10 25 50

← PrevPage 40 of 369Next →

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