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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 661–670 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction	Sep 9, 2022	Emotion-Cause Pair ExtractionMulti-Task Learning	CodeCode Available	0	5
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction	Nov 7, 2023	AttributeAttribute Value Extraction	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
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings	Sep 14, 2022	FormMulti-Task Learning	CodeCode Available	0	5
Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection	Dec 16, 2019	Multi-Task Learning	CodeCode Available	0	5
Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy	Feb 19, 2021	Active LearningHierarchical Reinforcement Learning	CodeCode Available	0	5
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity	Sep 5, 2019	Language ModelingLanguage Modelling	CodeCode Available	0	5
How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization	Dec 31, 2021	Gaussian ProcessesL2 Regularization	CodeCode Available	0	5
Multi-task Learning for Influence Estimation and Maximization	Apr 18, 2019	Multi-Task LearningRepresentation 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