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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 1041–1050 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function Classes	Apr 4, 2024	In-Context LearningMulti-Task Learning	CodeCode Available	0	5
Adaptive and Robust Multi-Task Learning	Feb 10, 2022	Multi-Task Learning	CodeCode Available	0	5
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge Distillation	Dec 24, 2024	Computational EfficiencyHandwritten Text Recognition	CodeCode Available	0	5
Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans	Mar 9, 2023	Image SegmentationMedical Image Segmentation	CodeCode Available	0	5
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples	Nov 7, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0	5
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks	Oct 9, 2022	DecoderImage Segmentation	CodeCode Available	0	5
Compressed Hierarchical Representations for Multi-Task Learning and Task Clustering	May 31, 2022	ClusteringMulti-Task Learning	CodeCode Available	0	5
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling	Apr 30, 2021	ClassificationGeneral Classification	CodeCode Available	0	5
A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in Sanskrit	Aug 22, 2022	Dependency ParsingMorphological Tagging	CodeCode Available	0	5
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks	Nov 14, 2018	Inductive BiasMulti-Task 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