FastCAR: Fast Classification And Regression Multi-Task Learning via Task
  Consolidation for Modelling a Continuous Property Variable of Object Classes
Anoop Kini Andreas Jansche Timo Bernthaler Gerhard Schneider
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 2.3%). The experiments performed used an 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 the labeling transformation and single-task regression network
architecture, FastCAR achieves reduced latency and time efficiency.