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 2176–2200 of 3687 papers

TitleStatusHype
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models—0
Transductive Learning for Multi-Task Copula Processes—0
Transductive Matrix Completion with Calibration for Multi-Task Learning—0
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning—0
Transfer and Multi-Task Learning for Noun--Noun Compound Interpretation—0
Transfer Learning and Augmentation for Word Sense Disambiguation—0
Transfer Learning for Speech and Language Processing—0
Transfer Learning for the Prediction of Entity Modifiers in Clinical Text: Application to Opioid Use Disorder Case Detection—0
Transfer Learning from Partial Annotations for Whole Brain Segmentation—0
Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors—0
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging—0
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images—0
Transformative Machine Learning—0
Transformer-based Approach for Predicting Chemical Compound Structures—0
Transformer-based Multimodal Information Fusion for Facial Expression Analysis—0
Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets—0
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation—0
Transitive Invariance for Self-supervised Visual Representation Learning—0
Translation Quality Estimation by Jointly Learning to Score and Rank—0
Transparent text quality assessment with convolutional neural networks—0
Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks—0
Interacting with Explanations through Critiquing—0
Tree DNN: A Deep Container Network—0
Tricks for Training Sparse Translation Models—0
TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval—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