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 2426–2450 of 3687 papers

TitleStatusHype
Learning Multiple Visual Tasks while Discovering their Structure—0
Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Learning Predictive, Online Approximations of Explanatory, Offline Algorithms—0
Learning Rates for Multi-task Regularization Networks—0
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
Learning representations for sentiment classification using Multi-task framework—0
Learning Representations for Text-level Discourse Parsing—0
Learning Shared Dynamics with Meta-World Models—0
Learning Task Grouping and Overlap in Multi-task Learning—0
Learning Task Relatedness in Multi-Task Learning for Images in Context—0
Learning to Branch for Multi-Task Learning—0
Learning to Generate Questions by Learning What not to Generate—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
Learning to Jointly Generate and Separate Reflections—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Multi-Task Learn for Better Neural Machine Translation—0
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps—0
Learning to optimize by multi-gradient for multi-objective optimization—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
Learning to Push by Grasping: Using multiple tasks for effective learning—0
Learning to Recommend with Multiple Cascading Behaviors—0
Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection—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