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.

翻译：FastCAR是一种新颖的任务整合方法，专为多任务学习中的分类与回归任务设计，尽管这两类任务存在异质性且仅具有微弱相关性。该方法解决了对象分类与连续属性变量回归问题，这是科学与工程领域的关键应用场景。FastCAR采用标签转换策略，可与单一任务回归网络架构结合使用。在综合考虑两个任务的学习效果时（分类准确率99.54%，回归平均绝对百分比误差2.3%），FastCAR优于传统多任务学习模型族（这些模型族在架构与损失加权方案的参数空间中进行调优）。实验采用我们贡献的先进钢材属性数据集，包含4536张224×224像素图像，每张图像标注了对象类别及连续取值的硬度属性。通过标签转换与单一任务回归网络架构，FastCAR实现了更低的延迟与更高的时间效率。