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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 421–430 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding	May 22, 2024	DecoderMulti-Task Learning	CodeCode Available	1	5
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification	Jul 27, 2021	DiagnosticLesion Classification	CodeCode Available	1	5
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training	May 31, 2024	HallucinationMulti-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
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning	Oct 25, 2021	Multi-Task Learning	CodeCode Available	1	5
Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT	May 19, 2022	Multi-Task Learningnamed-entity-recognition	CodeCode Available	1	5
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems	Aug 27, 2018	Multi-Task Learningquantile regression	CodeCode Available	1	5
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition	Feb 22, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
Evaluating Logical Generalization in Graph Neural Networks	Mar 14, 2020	Continual LearningDiversity	CodeCode Available	1	5
Gradient Surgery for Multi-Task Learning	Jan 19, 2020	Deep Reinforcement Learningimage-classification	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