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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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3501–3525 of 3687 papers

TitleStatusHype
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition—0
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task—0
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning—0
Jointly learning heterogeneous features for rgb-d activity recognition—0
Distributed Multi-Task Relationship Learning—0
An Attention-Driven Approach of No-Reference Image Quality Assessment—0
Learning to Detect Multiple Photographic DefectsCode0
Intra-day Activity Better Predicts Chronic Conditions—0
融合多任務學習類神經網路聲學模型訓練於會議語音辨識之研究 (Leveraging Multi-Task Learning with Neural Network Based Acoustic Modeling for Improved Meeting Speech Recognition) [In Chinese]—0
Joint quantile regression in vector-valued RKHSs—0
Adaptive Smoothed Online Multi-Task Learning—0
Quality Estimation for Language Output Applications—0
A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks—0
A Unified Architecture for Semantic Role Labeling and Relation Classification—0
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives—0
Deep Clustering and Conventional Networks for Music Separation: Stronger Together—0
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute ClassificationCode0
Action2Activity: Recognizing Complex Activities from Sensor Data—0
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP TasksCode0
Multi-task learning with deep model based reinforcement learning—0
An All-In-One Convolutional Neural Network for Face AnalysisCode0
Posterior regularization for Joint Modeling of Multiple Structured Prediction Tasks with Soft Constraints—0
Exploiting Source-side Monolingual Data in Neural Machine Translation—0
Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition—0
A General Regularization Framework for Domain Adaptation—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