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 926–950 of 3687 papers

TitleStatusHype
Better Together: Using Multi-task Learning to Improve Feature Selection within Structural Datasets—0
Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation—0
Better Transfer Learning with Inferred Successor Maps—0
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images—0
A convex optimization formulation for multivariate regression—0
DSReg: Using Distant Supervision as a Regularizer—0
DT4ECG: A Dual-Task Learning Framework for ECG-Based Human Identity Recognition and Human Activity Detection—0
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning—0
Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration—0
Exceeding the Limits of Visual-Linguistic Multi-Task Learning—0
Exploiting Source-side Monolingual Data in Neural Machine Translation—0
Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance—0
DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents—0
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering—0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation—0
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models—0
Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach—0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning—0
Beyond without Forgetting: Multi-Task Learning for Classification with Disjoint Datasets—0
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification—0
Deep Learning based Positioning with Multi-task Learning and Uncertainty-based Fusion—0
Deep Learning Approach for Matrix Completion Using Manifold Learning—0
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks—0
Deep Latent Emotion Network for Multi-Task Learning—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