Recent progress of deep learning has empowered various intelligent transportation applications, especially in car-sharing platforms. While the traditional operations of the car-sharing service highly relied on human engagements in fleet management, modern car-sharing platforms let users upload car images before and after their use to inspect the cars without a physical visit. To automate the aforementioned inspection task, prior approaches utilized deep neural networks. They commonly employed pre-training, a de-facto technique to establish an effective model under the limited number of labeled datasets. As candidate practitioners who deal with car images would presumably get suffered from the lack of a labeled dataset, we analyzed a sophisticated analogy into the effectiveness of pre-training is important. However, prior studies primarily shed a little spotlight on the effectiveness of pre-training. Motivated by the aforementioned lack of analysis, our study proposes a series of analyses to unveil the effectiveness of various pre-training methods in image recognition tasks at the car-sharing platform. We set two real-world image recognition tasks in the car-sharing platform in a live service, established them under the many-shot and few-shot problem settings, and scrutinized which pre-training method accomplishes the most effective performance in which setting. Furthermore, we analyzed how does the pre-training and fine-tuning convey different knowledge to the neural networks for a precise understanding.

翻译：深度学习的最新进展推动了各种智能交通应用的发展，特别是在共享汽车平台中。传统的共享汽车服务运营高度依赖人工参与车队管理，而现代共享汽车平台允许用户在使用前后上传车辆图像，以便无需实地检查即可对车辆进行检测。为自动化上述检测任务，以往的方法采用了深度神经网络。这些方法通常使用预训练技术，这是一种在标注数据集有限的情况下建立有效模型的行业标准方法。由于处理车辆图像的实践者可能因缺乏标注数据集而面临困难，我们深入分析了预训练有效性的复杂类比及其重要性。然而，以往的研究对预训练有效性的关注甚少。针对上述分析不足，我们的研究提出了一系列分析，旨在揭示共享汽车平台图像识别任务中各种预训练方法的有效性。我们设置了共享汽车平台实际服务中的两个真实图像识别任务，将其建立在多样本和少样本问题场景下，并仔细检验了哪种预训练方法在何种场景下能实现最有效的性能。此外，我们还分析了预训练和微调如何向神经网络传递不同的知识，以实现精确的理解。