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FastCAR: Fast Classification And Regression Multi-Task Learning via Task Consolidation for Modelling a Continuous Property Variable of Object Classes

arXiv 2403.17926 ↗First posted 26 Mar 2024cs.CVcited by 0

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Abstract (arXiv)

FastCAR is a novel task consolidation approach in Multi-Task Learning (MTL) for a classification and a regression task, despite task heterogeneity with only subtle correlation. It addresses object classification and continuous property variable regression, a crucial use case in science and engineering. FastCAR involves a labeling transformation approach that can be used with a single-task regression network architecture. FastCAR outperforms traditional MTL model families, parametrized in the landscape of architecture and loss weighting schemes, when learning of both tasks are collectively considered (classification accuracy of 99.54%, regression mean absolute percentage error of 2.4%). The experiments performed used an Advanced Steel Property dataset https://github.com/fastcandr/Advanced-Steel-Property-Dataset contributed by us. The dataset comprises 4536 images of 224x224 pixels, annotated with object classes and hardness properties that take continuous values. With our designed approach, FastCAR achieves reduced latency and time efficiency.

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References · 20

  1. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  2. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  3. 2017A Survey on Multi-Task Learningcited by 840
  4. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  5. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  6. 2018End-to-End Multi-Task Learning with Attentioncited by 534
  7. 2016Cross-stitch Networks for Multi-task Learningcited by 467
  8. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
  9. 2020Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detectioncited by 409
  10. 2019Which Tasks Should Be Learned Together in Multi-task Learning?cited by 375
  11. 2021TOOD: Task-aligned One-stage Object Detectioncited by 218
  12. 2022Multi-Task Learning as a Bargaining Gamecited by 215
8 more
  1. 2020Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropoutcited by 201
  2. 2021OTA: Optimal Transport Assignment for Object Detectioncited by 178
  3. 2020Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Modelscited by 169
  4. 2020Learning to Branch for Multi-Task Learningcited by 115
  5. 2021Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learningcited by 101
  6. 2023Independent Component Alignment for Multi-Task Learningcited by 70
  7. 2019MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learningcited by 45
  8. 2018Evolutionary Neural Architecture Search for Image Restorationcited by 12

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