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 1976–2000 of 3687 papers

TitleStatusHype
SoftNER: Mining Knowledge Graphs From Cloud Incidents—0
A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks—0
Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking—0
Towards Invariant Soft Biometrics from Electrocardiograms—0
Mint: Matrix-Interleaving for Multi-Task Learning—0
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models—0
MIRACLE: Multi-task Learning based Interpretable Regulation of Autoimmune Diseases through Common Latent Epigenetics—0
Mirror U-Net: Marrying Multimodal Fission with Multi-task Learning for Semantic Segmentation in Medical Imaging—0
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems—0
Mitigating Bias for Question Answering Models by Tracking Bias Influence—0
Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning—0
A Unified Perspective on Multi-Domain and Multi-Task Learning—0
Mitigating Hidden Confounding Effects for Causal Recommendation—0
Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies—0
Towards Language-Universal End-to-End Speech Recognition—0
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model—0
MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering—0
Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning—0
Mixed-norm Regularization for Brain Decoding—0
Mixed-Supervised Dual-Network for Medical Image Segmentation—0
Mixed Supervised Graph Contrastive Learning for Recommendation—0
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting—0
Mixpert: Mitigating Multimodal Learning Conflicts with Efficient Mixture-of-Vision-Experts—0
Mixtraining: A Better Trade-Off Between Compute and Performance—0
Mixture of Latent Experts Using Tensor Products—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