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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 481–490 of 3687 papers

Title	Date	Tasks	Status	Hype
RRPN++: Guidance Towards More Accurate Scene Text Detection	Sep 28, 2020	Multi-Task LearningScene Text Detection	CodeCode Available	1
Label-Efficient Multi-Task Segmentation using Contrastive Learning	Sep 23, 2020	Contrastive LearningMulti-Task Learning	CodeCode Available	1
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data	Sep 19, 2020	Multi-Task LearningNatural Language Inference	CodeCode Available	1
Multi-Loss Weighting with Coefficient of Variations	Sep 3, 2020	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
A Survey on Negative Transfer	Sep 2, 2020	Lifelong learningMulti-Task Learning	CodeCode Available	1
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection	Aug 17, 2020	Event DetectionMultiple Instance Learning	CodeCode Available	1
MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like Diseases Diagnosis From X-ray Scans	Aug 5, 2020	Multi-Task LearningTransfer Learning	CodeCode Available	1
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit	Aug 1, 2020	DiversityMulti-Task Learning	CodeCode Available	1
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference	Jul 24, 2020	Incremental LearningMulti-Task Learning	CodeCode Available	1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data	Jul 20, 2020	Few-Shot LearningMulti-Task Learning	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