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Design Perspectives of Multitask Deep Learning Models and Applications

arXiv 2209.13444 ↗First posted 27 Sep 2022cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric better. Under learning-related tasks, multi-task learning has been able to generalize the models even better. We try to enhance the feature mapping of the multi-tasking models by sharing features among related tasks and inductive transfer learning. Also, our interest is in learning the task relationships among various tasks for acquiring better benefits from multi-task learning. In this chapter, our objective is to visualize the existing multi-tasking models, compare their performances, the methods used to evaluate the performance of the multi-tasking models, discuss the problems faced during the design and implementation of these models in various domains, and the advantages and milestones achieved by them

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References · 18

  1. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  2. 2017A Survey on Multi-Task Learningcited by 838
  3. 2016Cross-stitch Networks for Multi-task Learningcited by 466
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  5. 2016HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognitioncited by 103
  6. 2015Multi-task Sequence to Sequence Learningcited by 94
  7. 2016Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classificationcited by 93
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  10. 2016Trace Norm Regularised Deep Multi-Task Learningcited by 50
  11. 2019Pedestrian Attribute Recognition: A Surveycited by 49
  12. 2017Latent Multi-task Architecture Learningcited by 29
6 more
  1. 2017Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalitiescited by 25
  2. 2015Multi-domain Dialog State Tracking using Recurrent Neural Networkscited by 14
  3. 2014Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognitioncited by 12
  4. 2019Multitask Classification and Segmentation for Cancer Diagnosis in Mammographycited by 9
  5. 2020Multi-task deep learning for image segmentation using recursive approximation taskscited by 3
  6. 2014A Two-Part Mixed-Effects Modeling Framework For Analyzing Whole-Brain Network Datacited by 2

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