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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 381–390 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
A Frustratingly Easy Approach for Entity and Relation Extraction	Oct 24, 2020	Joint Entity and Relation ExtractionMulti-Task Learning	CodeCode Available	1	5
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis	Aug 10, 2023	Malware AnalysisMulti-Task Learning	CodeCode Available	1	5
Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning	Oct 27, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts	Jun 4, 2019	Emotion Cause ExtractionEmotion-Cause Pair Extraction	CodeCode Available	1	5
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need	Dec 1, 2020	AllMulti-Task Learning	CodeCode Available	1	5
MobIE: A German Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain	Aug 16, 2021	Entity LinkingMulti-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
Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic	Jul 9, 2024	ClassificationDisentanglement	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
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization	Feb 14, 2021	Federated LearningFew-Shot Learning	CodeCode Available	1	5

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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