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

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Showing 2651–2675 of 3687 papers

TitleStatusHype
Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identificationCode1
Cross Lingual Cross Corpus Speech Emotion Recognition—0
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images—0
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Interpretable MTL from Heterogeneous Domains using Boosted Tree—0
Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes—0
Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder—0
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets—0
Evaluating Logical Generalization in Graph Neural NetworksCode1
OccuSeg: Occupancy-aware 3D Instance Segmentation—0
Inducing Optimal Attribute Representations for Conditional GANs—0
Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point CloudsCode1
Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face—0
AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted SurgeryCode0
Multi-task Learning Based Neural Bridging Reference ResolutionCode0
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?Code0
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency ServicesCode0
Multi-Task Learning with Auxiliary Speaker Identification for Conversational Emotion Recognition—0
Bayesian Neural Networks With Maximum Mean Discrepancy RegularizationCode0
Hand-Priming in Object Localization for Assistive Egocentric Vision—0
Indoor Scene Recognition in 3DCode1
Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension—0
Evolving Losses for Unsupervised Video Representation Learning—0
Lipschitz standardization for multivariate learningCode1
End-to-end Emotion-Cause Pair Extraction via Learning to LinkCode1
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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