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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 811–820 of 3687 papers

Title	Date	Tasks	Status	Hype
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT	Oct 7, 2023	Audio captioningAutomatic Speech Recognition	CodeCode Available	2
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services	Oct 6, 2023	Hate Speech DetectionMulti-Task Learning	CodeCode Available	1
IBCL: Zero-shot Model Generation for Task Trade-offs in Continual Learning	Oct 4, 2023	Continual Learningimage-classification	CodeCode Available	0
Attention-based Multi-task Learning for Base Editor Outcome Prediction	Oct 4, 2023	Multi-Task Learning	—Unverified	0
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions	Oct 4, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
AdaMerging: Adaptive Model Merging for Multi-Task Learning	Oct 4, 2023	modelMulti-Task Learning	CodeCode Available	1
Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse	Oct 3, 2023	feature selectionimage-classification	CodeCode Available	0
Fine-tuned vs. Prompt-tuned Supervised Representations: Which Better Account for Brain Language Representations?	Oct 3, 2023	ChunkingMulti-Task Learning	—Unverified	0
Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models	Oct 2, 2023	Deep LearningMulti-Task Learning	—Unverified	0
Multi-task Learning with 3D-Aware Regularization	Oct 2, 2023	Depth EstimationMulti-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