SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1026–1050 of 3687 papers

TitleStatusHype
MKD: a Multi-Task Knowledge Distillation Approach for Pretrained Language Models—0
3D Instance Segmentation via Multi-Task Metric Learning—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Attentive max feature map and joint training for acoustic scene classification—0
A Modular-based Strategy for Mitigating Gradient Conflicts in Simultaneous Speech Translation—0
Addressing Negative Transfer in Diffusion Models—0
Deep Clustering and Conventional Networks for Music Separation: Stronger Together—0
Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT—0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation—0
Attention-based Multi-task Learning for Base Editor Outcome Prediction—0
Deep Belief Networks Based Feature Generation and Regression for Predicting Wind Power—0
AMMSM: Adaptive Motion Magnification and Sparse Mamba for Micro-Expression Recognition—0
Federated Multi-Objective Learning—0
Deep Automated Multi-task Learning—0
DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications—0
Attention-based Multi-task Learning for Base Editor Outcome Prediction—0
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images—0
A Mixed Learning Objective for Neural Machine Translation—0
A Benchmark for Federated Hetero-Task Learning—0
When MAML Can Adapt Fast and How to Assist When It Cannot—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing—0
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All datasetsQM9Cityscapes testNYUv2OMNIGLOTCelebAUTKFacewireframe dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7—Unverified
2CAGrad∆m%112.8—Unverified
3IMTL-G∆m%77.2—Unverified
4Nash-MTL∆m%62—Unverified
5BayesAgg-MTL∆m%53.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41—Unverified
2Nash-MTLmIoU75.41—Unverified
3MultiObjectiveOptimizationmIoU66.63—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14—Unverified
2Nash-MTLMean IoU40.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52—Unverified
2Mixture-of-ExpertsAverage Accuracy92.19—Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3—Unverified