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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

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Showing 3301–3325 of 3687 papers

TitleStatusHype
Diversity and Depth in Per-Example Routing Models—0
SpeechX: Neural Codec Language Model as a Versatile Speech Transformer—0
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
DLM-VMTL:A Double Layer Mapper for heterogeneous data video Multi-task prompt learning—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of Freedom with Multi-Task Learning—0
Docking-based Virtual Screening with Multi-Task Learning—0
Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text—0
Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches—0
Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension—0
Does Learning Specific Features for Related Parts Help Human Pose Estimation?—0
Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games—0
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding—0
Domain Adversarial Neural Networks for Dysarthric Speech Recognition—0
Domain Adversarial Training for Accented Speech Recognition—0
Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus—0
Domain Generalization for In-Orbit 6D Pose Estimation—0
A Generalized Zero-Shot Framework for Emotion Recognition from Body Gestures—0
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning—0
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain—0
Variational 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