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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 1411–1420 of 3687 papers

Title	Date	Tasks	Status	Hype
Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation	Jul 2, 2022	Knowledge DistillationMulti-Task Learning	—Unverified	0
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks	Jul 1, 2022	Knowledge DistillationMulti-Task Learning	—Unverified	0
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining	Jul 1, 2022	Intent DiscoveryLanguage Modeling	—Unverified	0
PAIC at SemEval-2022 Task 5: Multi-Modal Misogynous Detection in MEMES with Multi-Task Learning And Multi-model Fusion	Jul 1, 2022	Binary ClassificationClassification	—Unverified	0
taochen at SemEval-2022 Task 5: Multimodal Multitask Learning and Ensemble Learning	Jul 1, 2022	Ensemble LearningMulti-Task Learning	—Unverified	0
MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets	Jul 1, 2022	Multi-Task LearningSentence	CodeCode Available	0
Zero-shot Entity Linking with Less Data	Jul 1, 2022	Entity LinkingMulti-Task Learning	CodeCode Available	3
LITE: Intent-based Task Representation Learning Using Weak Supervision	Jul 1, 2022	ManagementMulti-Task Learning	CodeCode Available	0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps	Jul 1, 2022	Multi-Task LearningNatural Language Understanding	—Unverified	0
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs	Jul 1, 2022	Multi-Task LearningResponse Generation	—Unverified	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