Handling Distribution Shift in Tire DesignDownload PDF

09 Oct 2021, 14:49 (modified: 01 Dec 2021, 13:35)NeurIPS 2021 Workshop DistShift PosterReaders: Everyone
Keywords: real-world application, domain adaptation benchmark, evaluation metric, regression, industrial design, tire design, industrial deployment
TL;DR: This work presents a benchmark of domain adaptation methods on a real-world application of tire design as well as an evaluation process for model selection in this context.
Abstract: The recent success of machine learning methods in the industrial sector open new perspectives for the design of innovative products. However, these promising results are often challenged when it comes to industrial model deployment. Indeed, it frequently appears that the performance of the model is degraded when used on application data due to the distribution shift between the training and the targeted data. This issue is even more critical for model dedicated to the research of innovative designs as the model is mainly used on unseen regions of the design space. In this work, we present, on a real application of tire design, how distribution shifts impact the model performance and what can be expected from several domain adaptation methods. In an objective of industrial model deployment, we conduct this benchmark with the use of unsupervised evaluation metrics that considerably help the model selection.
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