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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–925 of 3687 papers

TitleStatusHype
Extracting Fractional Inspiratory Time from Electrocardiograms—0
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving—0
Enhancing Self-Attention with Knowledge-Assisted Attention Maps—0
DLM-VMTL:A Double Layer Mapper for heterogeneous data video Multi-task prompt learning—0
Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Docking-based Virtual Screening with Multi-Task Learning—0
Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation—0
A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics—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
Explainable Identification of Dementia from Transcripts using Transformer Networks—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
A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction—0
Domain Generalization for In-Orbit 6D Pose Estimation—0
BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification—0
BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification—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
A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotions—0
Exploiting Source-side Monolingual Data in Neural Machine Translation—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