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 3376–3400 of 3687 papers

TitleStatusHype
Geographic Adaptation of Pretrained Language ModelsCode0
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction DownscalingCode0
SEE: Continual Fine-tuning with Sequential Ensemble of ExpertsCode0
On Identifying Hashtags in Disaster Twitter DataCode0
AnywhereDoor: Multi-Target Backdoor Attacks on Object DetectionCode0
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian ManifoldsCode0
Model-Protected Multi-Task LearningCode0
Segmentation-Consistent Probabilistic Lesion CountingCode0
Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal NetworksCode0
Online Parallel Multi-Task Relationship Learning via Alternating Direction Method of MultipliersCode0
Online Parameter-Free Learning of Multiple Low Variance TasksCode0
On Low-rank Trace Regression under General Sampling DistributionCode0
Dr.VOT : Measuring Positive and Negative Voice Onset Time in the WildCode0
Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task DivisionCode0
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-RankingCode0
Generative Domain-Migration Hashing for Sketch-to-Image RetrievalCode0
Generalizing Natural Language Analysis through Span-relation RepresentationsCode0
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identificationCode0
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task LearningCode0
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple DocumentsCode0
Revisiting Model's Uncertainty and Confidences for Adversarial Example DetectionCode0
On the Automatic Generation of Medical Imaging ReportsCode0
Morphological Segmentation for SenecaCode0
Traffic flow prediction using Deep Sedenion NetworksCode0
DRAGNN: A Transition-based Framework for Dynamically Connected Neural NetworksCode0
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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