BIKED：基于机器学习基准的计算自行车设计数据集
BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks
摘要：
在本文中，我们提出了“BIKED”，一个数据集包括4500个独立设计的自行车模型，来自数百个设计师。我们希望BIKED能够为自行车提供各种数据驱动的设计应用，并支持数据驱动设计方法的开发。该数据集由各种设计信息组成，包括组件图像、零部件图像、数字设计参数和类别标签。在本文中，我们首先讨论了数据集的处理，然后强调了一些突出的研究问题，BIKED可以帮助解决。在这些问题中，我们进一步详细探讨了以下几个问题：1）当前自行车市场和设计空间是否存在突出的差距？我们使用无监督降维方法探索设计空间。2)如何确定自行车的类别，以及哪些因素在定义自行车时起着关键作用？我们通过使用不同形式的设计数据训练众多分类器，并通过基于排列的可解释性分析识别具有特殊意义的参数，来解决自行车分类任务。3)如何合成新的自行车使用不同的表示方法？我们考虑了许多机器学习方法来生成新的自行车模型，以及使用变分自编码器在现有模型之间插值和外推。数据集和代码可在http://decode.mit.edu/projects/biked/上获得。
英文摘要：
In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: 1) Are there prominent gaps in the current bicycle market and design space? We explore the design space using unsupervised dimensionality reduction methods. 2) How does one identify the class of a bicycle and what factors play a key role in defining it? We address the bicycle classification task by training a multitude of classifiers using different forms of design data and identifying parameters of particular significance through permutation-based interpretability analysis. 3) How does one synthesize new bicycles using different representation methods? We consider numerous machine learning methods to generate new bicycle models as well as interpolate between and extrapolate from existing models using Variational Autoencoders. The dataset and code are available at http://decode.mit.edu/projects/biked/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
