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 1526–1550 of 3687 papers

TitleStatusHype
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning—0
Findings of the Shared Task on Multi-task Learning in Dravidian Languages—0
Independent Component Alignment for Multi-Task Learning—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—0
Inducing Optimal Attribute Representations for Conditional GANs—0
InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation—0
Inference-Time Personalized Federated Learning—0
On-Demand Unlabeled Personalized Federated Learning—0
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification—0
Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts—0
Joint shape learning and segmentation for medical images using a minimalistic deep network—0
Infinite Latent SVM for Classification and Multi-task Learning—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Infinite-Task Learning with RKHSs—0
Audio-Visual Speech Inpainting with Deep Learning—0
A Multi-modal and Multi-task Learning Method for Action Unit and Expression Recognition—0
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning—0
Deeply Learned Attributes for Crowded Scene Understanding—0
Injecting Imbalance Sensitivity for Multi-Task Learning—0
InLINE: Inner-Layer Information Exchange for Multi-task Learning on Heterogeneous Graphs—0
Few-shot Segmentation with Optimal Transport Matching and Message Flow—0
Composite Learning for Robust and Effective Dense Predictions—0
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning—0
Few-Shot Preference Learning for Human-in-the-Loop RL—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