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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 3026–3050 of 3687 papers

TitleStatusHype
Locale-agnostic Universal Domain Classification Model in Spoken Language Understanding—0
Agnostic Lane Detection—0
Multi-task Learning with Gradient Communication—0
Diversity and Depth in Per-Example Routing Models—0
Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch—0
TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING—0
Understand the dynamics of GANs via Primal-Dual Optimization—0
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning—0
Activity Regularization for Continual Learning—0
Routing Networks and the Challenges of Modular and Compositional ComputationCode0
Decentralized Multi-Task Learning Based on Extreme Learning Machines—0
DynamoNet: Dynamic Action and Motion Network—0
Multi-Task Learning for Argumentation Mining—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Unifying Question Answering, Text Classification, and Regression via Span Extraction—0
Multi-task Learning for Influence Estimation and MaximizationCode0
On Low-rank Trace Regression under General Sampling DistributionCode0
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning—0
Low-Rank Deep Convolutional Neural Network for Multi-Task Learning—0
Strong Baselines for Complex Word Identification across Multiple LanguagesCode0
Context-Aware Embeddings for Automatic Art AnalysisCode0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and ExtractionCode0
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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