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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 2801–2810 of 3687 papers

Title	Date	Tasks	Status	Hype
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning	Oct 24, 2019	Meta-LearningMeta Reinforcement Learning	CodeCode Available	2
Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed	Oct 24, 2019	Machine TranslationMulti-Task Learning	—Unverified	0
Deep generative model-driven multimodal prostate segmentation in radiotherapy	Oct 23, 2019	Medical Image AnalysisMulti-Task Learning	—Unverified	0
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension	Oct 22, 2019	Multi-Task LearningQuestion Answering	CodeCode Available	0
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning	Oct 16, 2019	Multi-Task LearningRecommendation Systems	—Unverified	0
Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis	Oct 16, 2019	Multi-Task LearningPrediction	—Unverified	0
Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction	Oct 15, 2019	Click-Through Rate PredictionMulti-Task Learning	—Unverified	0
Self-supervised Label Augmentation via Input Transformations	Oct 14, 2019	Data Augmentationimbalanced classification	CodeCode Available	0
SUM: Suboptimal Unitary Multi-task Learning Framework for Spatiotemporal Data Prediction	Oct 11, 2019	Meta-LearningMulti-Task Learning	—Unverified	0
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base	Oct 11, 2019	Conversational Question AnsweringMulti-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