In this paper, we systematically analyze unsupervised domain adaptation
pipelines for object classification in a challenging industrial setting. In
contrast to standard natural object benchmarks existing in the field, our
results highlight the most important design choices when only category-labeled
CAD models are available but classification needs to be done with real-world
images. Our domain adaptation pipeline achieves SoTA performance on the VisDA
benchmark, but more importantly, drastically improves recognition performance
on our new open industrial dataset comprised of 102 mechanical parts. We
conclude with a set of guidelines that are relevant for practitioners needing
to apply state-of-the-art unsupervised domain adaptation in practice. Our code
is available at this https URL

通过分析工业环境中无监督领域适应的管道，本文系统地研究了目标分类。与领域中现有的标准自然目标基准相比，我们的结果突出了当只有分类标记的 CAD 模型可用但需要使用真实世界图像进行分类时的最重要的设计选择。我们的领域适应管道在 VisDA 基准上取得了最先进的性能，但更重要的是，在我们的新的开放式工业数据集中，显着提高了识别性能。最后，我们得出了一套对于需要实际应用最先进的无监督领域适应的从业者而言相关的指南。我们的代码可以在此 https URL 找到。

CAD 模型到现实世界图像：工业物体分类中无监督领域自适应的实用方法

CAD Models to Real-World Images: A Practical Approach to Unsupervised  Domain Adaptation in Industrial Object Classification

Intelligent Process Automation (IPA) is an emerging technology with a primary
goal to assist the knowledge worker by taking care of repetitive, routine and
low-cognitive tasks. Conversational agents that can interact with users in a
natural language are potential application for IPA systems. Such intelligent
agents can assist the user by answering specific questions and executing
routine tasks that are ordinarily performed in a natural language (i.e.,
customer support). In this work, we tackle a challenge of implementing an IPA
conversational assistant in a real-world industrial setting with a lack of
structured training data. Our proposed system brings two significant benefits:
First, it reduces repetitive and time-consuming activities and, therefore,
allows workers to focus on more intelligent processes. Second, by interacting
with users, it augments the resources with structured and to some extent
labeled training data. We showcase the usage of the latter by re-implementing
several components of our system with Transfer Learning (TL) methods.

本文介绍在缺乏结构化训练数据的真实工业场景中实现智能过程自动化 (IPA) 交谈助手的挑战，以及使用迁移学习方法重新实现自动化系统多个组件，从而为用户提供结构化且在某种程度上标记的培训数据。