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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 641–650 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization	Mar 2, 2020	image-classificationImage Classification	CodeCode Available	0	5
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models	Jul 31, 2024	Instruction FollowingMulti-Task Learning	CodeCode Available	0	5
Joint Acne Image Grading and Counting via Label Distribution Learning	Oct 1, 2019	Acne Severity GradingMedical Image Classification	CodeCode Available	0	5
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews	Jan 17, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0	5
Knowing Earlier what Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-Task Learning	Jul 6, 2022	DiagnosticMulti-Task Learning	CodeCode Available	0	5
Latent Entities Extraction: How to Extract Entities that Do Not Appear in the Text?	Oct 1, 2018	Multi-Task Learningnamed-entity-recognition	CodeCode Available	0	5
Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language	Mar 18, 2021	Multi-Task Learning	CodeCode Available	0	5
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes	Apr 10, 2017	Bayesian Inferencecounterfactual	CodeCode Available	0	5
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis	Jun 12, 2024	ClusteringDimensionality Reduction	CodeCode Available	0	5
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning	Oct 16, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	0	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