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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 1891–1900 of 3687 papers

Title	Date	Tasks	Status	Hype
Multitask Balanced and Recalibrated Network for Medical Code Prediction	Sep 6, 2021	Medical Code PredictionMulti-Task Learning	CodeCode Available	1
Transformer Models for Text Coherence Assessment	Sep 5, 2021	Coherence EvaluationMachine Translation	CodeCode Available	1
Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question Answering	Sep 4, 2021	Community Question AnsweringMulti-Task Learning	—Unverified	0
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting	Sep 2, 2021	AttributeMulti-Task Learning	CodeCode Available	1
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation	Sep 2, 2021	Clone DetectionCode Generation	CodeCode Available	1
HunterSpeechLab at GermEval 2021: Does Your Comment Claim A Fact? Contextualized Embeddings for German Fact-Claiming Comment Classification	Sep 1, 2021	Fact-Claiming Comment ClassificationMulti-Task Learning	—Unverified	0
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models	Aug 31, 2021	Multi-Task LearningTransfer Learning	—Unverified	0
Private Multi-Task Learning: Formulation and Applications to Federated Learning	Aug 30, 2021	BIG-bench Machine LearningDistributed Optimization	CodeCode Available	0
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification	Aug 29, 2021	ClassificationDisaster Response	CodeCode Available	1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation	Aug 28, 2021	Data AugmentationDepth Estimation	CodeCode Available	1

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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