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 2926–2950 of 3687 papers

TitleStatusHype
Tackling Ordinal Regression Problem for Heterogeneous Data: Sparse and Deep Multi-Task Learning Approaches—0
City classification from multiple real-world sound scenesCode0
A Multi-Task Self-Normalizing 3D-CNN to Infer Tuberculosis Radiological Manifestations—0
Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening—0
Multi-Stage Prediction Networks for Data Harmonization—0
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
Mixed-Supervised Dual-Network for Medical Image Segmentation—0
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning—0
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising—0
Generic Intent Representation in Web Search—0
Towards Adversarially Robust Object Detection—0
Quadruplet Selection Methods for Deep Embedding Learning—0
A Pragmatics-Centered Evaluation Framework for Natural Language UnderstandingCode0
Joint Learning of Named Entity Recognition and Entity Linking—0
News Cover Assessment via Multi-task Learning—0
OmniNet: A unified architecture for multi-modal multi-task learningCode0
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence—0
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch NetworkCode0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks—0
Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning—0
Graph-based Knowledge Distillation by Multi-head Attention NetworkCode0
Multi-Task Learning for Coherence ModelingCode0
Multi-Task Networks With Universe, Group, and Task Feature Learning—0
An Iteratively Re-weighted Method for Problems with Sparsity-Inducing Norms—0
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining—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