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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 3071–3080 of 3687 papers

Title	Date	Tasks	Status	Hype
Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation	Sep 25, 2019	Multi-Task LearningNamed Entity Recognition (NER)	CodeCode Available	0
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning	Nov 17, 2023	Multi-Task LearningOutlier Detection	CodeCode Available	0
SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition	Jul 19, 2022	Multi-Task Learning	CodeCode Available	0
Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis	Apr 28, 2020	DiagnosticMulti-Task Learning	CodeCode Available	0
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation	Oct 27, 2023	Conversational RecommendationDiversity	CodeCode Available	0
Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning	Nov 13, 2020	Language ModellingMulti-Task Learning	CodeCode Available	0
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation	Oct 21, 2021	Domain AdaptationMulti-Task Learning	CodeCode Available	0
Integrating Form and Meaning: A Multi-Task Learning Model for Acoustic Word Embeddings	Sep 14, 2022	FormMulti-Task Learning	CodeCode Available	0
Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing	Aug 19, 2022	Computational EfficiencyDecoder	CodeCode Available	0
An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning	Jul 22, 2022	Multi-Task Learning	CodeCode Available	0

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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