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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 3626–3650 of 3687 papers

TitleStatusHype
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Multi-Task Metric Learning on Network Data—0
Joint Emotion Analysis via Multi-task Gaussian Processes—0
Multi-task Sparse Structure Learning—0
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning—0
Conic Multi-Task ClassificationCode0
Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity—0
UoW: Multi-task Learning Gaussian Process for Semantic Textual Similarity—0
Self-Learning Camera: Autonomous Adaptation of Object Detectors to Unlabeled Video Streams—0
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network—0
Two-Stream Convolutional Networks for Action Recognition in VideosCode0
Transductive Learning for Multi-Task Copula Processes—0
SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation—0
A Recursive Recurrent Neural Network for Statistical Machine Translation—0
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share—0
Predicting Multiple Attributes via Relative Multi-task Learning—0
Surpassing Human-Level Face Verification Performance on LFW with GaussianFace—0
Pareto-Path Multi-Task Multiple Kernel Learning—0
Multi-task Feature Selection based Anomaly Detection—0
Mixed-norm Regularization for Brain Decoding—0
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems—0
A Fused Elastic Net Logistic Regression Model for Multi-Task Binary Classification—0
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds—0
Learning Feature Selection Dependencies in Multi-task Learning—0
Non-Linear Domain Adaptation with Boosting—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