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 281–290 of 3687 papers

Title	Date	Tasks	Status	Hype
Compressed Interaction Graph based Framework for Multi-behavior Recommendation	Mar 4, 2023	Multi-Task Learning	CodeCode Available	1
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation	May 24, 2023	GPULanguage Modeling	CodeCode Available	1
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning	Nov 15, 2020	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1
Context-Aware Answer Extraction in Question Answering	Nov 5, 2020	Multi-Task LearningPrediction	CodeCode Available	1
Continued Pretraining for Better Zero- and Few-Shot Promptability	Oct 19, 2022	Language ModelingLanguage Modelling	CodeCode Available	1
Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration	Jun 25, 2024	Change DetectionMulti-Task Learning	CodeCode Available	1
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems	Dec 1, 2021	Multi-Task Learning	CodeCode Available	1
CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing	May 28, 2025	Multi-Task LearningPrivacy Preserving	CodeCode Available	1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis	Jun 17, 2019	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality	Jul 1, 2020	Multimodal Sentiment AnalysisMulti-Task Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 29 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