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

Showing 551575 of 3687 papers

TitleStatusHype
Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto OptimizationCode1
Learning from Synthetic AnimalsCode1
Regularizing Deep Multi-Task Networks using Orthogonal GradientsCode1
CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task LearningCode1
Forgetting to learn logic programsCode1
On the Detection of Digital Face ManipulationCode1
Encoding Visual Attributes in Capsules for Explainable Medical DiagnosesCode1
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain SignalsCode1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in TextsCode1
CIF: Continuous Integrate-and-Fire for End-to-End Speech RecognitionCode1
RetinaFace: Single-stage Dense Face Localisation in the WildCode1
Large Scale Holistic Video UnderstandingCode1
What and How Well You Performed? A Multitask Learning Approach to Action Quality AssessmentCode1
Language Models are Unsupervised Multitask LearnersCode1
Peeking into the Future: Predicting Future Person Activities and Locations in VideosCode1
LEAF: A Benchmark for Federated SettingsCode1
Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problemsCode1
CentralNet: a Multilayer Approach for Multimodal FusionCode1
How emotional are you? Neural Architectures for Emotion Intensity Prediction in MicroblogsCode1
End-to-End Multi-Task Learning with AttentionCode1
The ApolloScape Open Dataset for Autonomous Driving and its ApplicationCode1
Semantic Line Detection and Its ApplicationsCode1
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and SemanticsCode1
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7Unverified
2CAGrad∆m%112.8Unverified
3IMTL-G∆m%77.2Unverified
4Nash-MTL∆m%62Unverified
5BayesAgg-MTL∆m%53.7Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41Unverified
2Nash-MTLmIoU75.41Unverified
3MultiObjectiveOptimizationmIoU66.63Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14Unverified
2Nash-MTLMean IoU40.13Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52Unverified
2Mixture-of-ExpertsAverage Accuracy92.19Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3Unverified