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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 1071–1080 of 3687 papers

Title	Date	Tasks	Status	Hype	Score
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation	May 1, 2022	Machine TranslationMulti-Task Learning	CodeCode Available	0	5
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks	Oct 9, 2022	DecoderImage Segmentation	CodeCode Available	0	5
Instruction Tuned Models are Quick Learners	May 17, 2023	In-Context LearningMulti-Task Learning	CodeCode Available	0	5
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction	Apr 6, 2019	Keyphrase GenerationMulti-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
Codec at SemEval-2022 Task 5: Multi-Modal Multi-Transformer Misogynous Meme Classification Framework	Jun 14, 2022	Binary ClassificationMeme Classification	CodeCode Available	0	5
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction	Feb 4, 2023	Few-Shot LearningMolecular Docking	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
An Exploratory Deep Learning Approach for Predicting Subsequent Suicidal Acts in Chinese Psychological Support Hotlines	Aug 29, 2024	Multi-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

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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