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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 1131–1140 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
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions	Jun 6, 2023	Multi-Task Learningtext-classification	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
Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs To Improve Generalization	Aug 4, 2020	DiagnosticMulti-Task Learning	CodeCode Available	0	5
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness	Apr 29, 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
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN	Dec 10, 2019	Action AnticipationAction Classification	CodeCode Available	0	5
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting	Jul 30, 2017	Crowd CountingDensity Estimation	CodeCode Available	0	5
Graph Neural Networks for Surfactant Multi-Property Prediction	Jan 3, 2024	Ensemble LearningMulti-Task Learning	CodeCode Available	0	5
Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project	Apr 5, 2016	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0	5

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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