SOTAVerified|Agents Browse Leaderboard About

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 1101–1110 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction	Feb 4, 2023	Few-Shot LearningMolecular Docking	CodeCode Available	0	5
Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators	Nov 11, 2022	DecoderMachine Translation	CodeCode Available	0	5
AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning	Apr 17, 2023	Multi-Task Learning	CodeCode Available	0	5
Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour	May 25, 2020	Multi-Task LearningNamed Entity Recognition (NER)	CodeCode Available	0	5
Helping Visually Impaired People Take Better Quality Pictures	May 14, 2023	Multi-Task Learning	CodeCode Available	0	5
City classification from multiple real-world sound scenes	Jul 29, 2019	Acoustic Scene ClassificationClassification	CodeCode Available	0	5
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization	Aug 4, 2020	DiagnosticMulti-Task Learning	CodeCode Available	0	5
An Ensemble Approach for Multiple Emotion Descriptors Estimation Using Multi-task Learning	Jul 22, 2022	Multi-Task Learning	CodeCode Available	0	5
Group Buying Recommendation Model Based on Multi-task Learning	Nov 25, 2022	Multi-Task LearningRepresentation Learning	CodeCode Available	0	5
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models	Dec 7, 2023	Multi-Task LearningPassage Retrieval	CodeCode Available	0	5

Show:10 25 50

← PrevPage 111 of 369Next →

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