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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 1151–1200 of 3687 papers

TitleStatusHype
"It's a Match!" -- A Benchmark of Task Affinity Scores for Joint Learning—0
Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations—0
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed ManipulationCode1
Anaphora Resolution in Dialogue: System Description (CODI-CRAC 2022 Shared Task)—0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Topic Segmentation Model Focusing on Local Context—0
Multi-Task Learning with Prior Information—0
Multi-Task Learning for Budbreak Prediction—0
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems—0
PMT-IQA: Progressive Multi-task Learning for Blind Image Quality AssessmentCode0
Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios—0
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Achievement-Based Training Progress Balancing for Multi-Task LearningCode0
Multi-Task Learning with Knowledge Distillation for Dense Prediction—0
CORE: Co-planarity Regularized Monocular Geometry Estimation with Weak Supervision—0
AdaMV-MoE: Adaptive Multi-Task Vision Mixture-of-Experts—0
Reconciling Object-Level and Global-Level Objectives for Long-Tail DetectionCode0
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network—0
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different DatasetsCode0
SmartAssign: Learning a Smart Knowledge Assignment Strategy for Deraining and Desnowing—0
Hierarchical Prompt Learning for Multi-Task Learning—0
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners—0
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual RecognitionCode0
Poly-PC: A Polyhedral Network for Multiple Point Cloud Tasks at Once—0
Multi-Label Compound Expression Recognition: C-EXPR Database & Network—0
Masked Representation Learning for Domain Generalized Stereo Matching—0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)—0
A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific—0
Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point CloudsCode1
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning—0
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs—0
Positive-incentive Noise—0
Dataless Knowledge Fusion by Merging Weights of Language ModelsCode1
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations—0
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational PerspectivesCode0
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks—0
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners—0
Hierarchical Over-the-Air FedGradNorm—0
Multi-task Learning for Cross-Lingual Sentiment AnalysisCode0
Egocentric Video Task Translation—0
Body Segmentation Using Multi-task Learning—0
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?Code0
InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation—0
An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images—0
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor DecompositionCode0
MAGVIT: Masked Generative Video TransformerCode2
Multi-task Learning for Personal Health Mention Detection on Social Media—0
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist ModelsCode1
Tree DNN: A Deep Container Network—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