Deep Design

Pan, Yulin; Burnap, Alexander; Hartley, Jeffrey; Gonzalez, Richard; Papalambros, Panos Y.

doi:10.1145/3097983.3098176

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…Building on the work from [5,6], which apply deep learning algorithms to assist the automotive aesthetic design, this paper proposes a computational framework for automotive exterior facelift, which provides intelligent decision support to automakers and designers. Our study aims to address the following research questions: Can a generative model be trained to present a design space for various automotive designs?…”

Section: -Jeremy Clarksonmentioning

confidence: 99%

“…As modern car designs have similar shapes and layouts, it is difficult for the regular deep models, with limited samples, to learn discriminating features for aesthetic ratings. Inspired by previous studies [9,5] that solve the problem through the metric learning approach, a double-task training frame is proposed in this paper, incorporating an angular loss-based classification [10,11,12] facilitate the learning of discriminating features. The decision optimiser is specified for selecting the best facelift plans, maximising profit over time.…”

Section: -Jeremy Clarksonmentioning

confidence: 99%

“…From a broader perspective, our study adds to the recent applications of machine learning and data mining in marketing and design studies. Different from previous studies, which have employed deep generative models for automotive exterior design [5,6,14], our study is the first to investigate automotive facelift that focuses on regional design upgrades and considers the design selection from a revenue maximisation perspective. In terms of technology, the proposed computational framework provides an end-to-end decision-support solution for automakers and automotive designers.…”

Section: -Jeremy Clarksonmentioning

confidence: 99%

“…Several existing studies [35,36,37] attempted to apply the CNN model to the categorisation of fashion products, achieving a remarkable accuracy. On the other hand, several studies relied on CNN-based models to predict the aesthetic level of durable products such as cars [38,5,6]. Researchers found that with sufficient images of cars and subjective labels, the CNN model could learn to make aesthetic ratings on diverse aspects such as sporty, appealing and innovative cars.…”

Section: Related Workmentioning

confidence: 99%

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GEO: A Computational Design Framework for Automotive Exterior Facelift

Huang

Chen

Yan

et al. 2023

ACM Trans. Knowl. Discov. Data

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Exterior facelift has become an effective method for automakers to boost the consumers’ interest in an existing car model before it is redesigned. To support the automotive facelift design process, this study develops a novel computational framework – Generator, Evaluator, Optimiser (GEO), which comprises 3 components: a StyleGAN2-based design generator that creates different facelift designs; a convolutional neural network (CNN)-based evaluator that assesses designs from the aesthetics perspective; and a recurrent neural network (RNN)-based decision optimiser that selects designs to maximise the predicted profit of the targeted car model over time. We validate the GEO framework in experiments with real-world datasets and describe some resulting managerial implications for automotive facelift. Our study makes both methodological and application contributions. First, the generator’s mapping network and projection methods are carefully tailored to facelift where only minor changes are performed without affecting the family signature of the automobile brands. Second, two evaluation metrics are proposed to assess the generated designs. Third, profit maximisation is taken into account in the design selection. From a high-level perspective, our study contributes to the recent use of machine learning and data mining in marketing and design studies. To the best of our knowledge, this is the first study that uses deep generative models for automotive regional design upgrading and that provides an end-to-end decision-support solution for automakers and designers.

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Section: -Jeremy Clarksonmentioning

confidence: 99%

Section: -Jeremy Clarksonmentioning

confidence: 99%

Section: -Jeremy Clarksonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

GEO: A Computational Design Framework for Automotive Exterior Facelift

Huang

Chen

Yan

et al. 2023

ACM Trans. Knowl. Discov. Data

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“…In the area of product design, Pedro et al [4] proposed to train a CNN with standard usability heuristics for evaluating usability, which is an easy method for evaluating usability in thermostats, based on images. Pan et al [5] used a scalable deep learning approach to predict and interpret customer perceptions of design attributes for heterogeneous markets. Wang et al [6] present a deep learning-based approach to automatically link customer needs to product design parameters.…”

Section: Introductionmentioning

confidence: 99%

Product Innovation Design Based on Deep Learning and Kansei Engineering

Quan

2018

Applied Sciences

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Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei engineering with back-propagation neural networks to establish a mapping model between product properties and styles. To address the inspiration issue in product innovation, the convolutional neural network-based neural style transfer is adopted to reconstruct and merge color and pattern features of the style image, which are then migrated to the target product. The generated new product image can not only preserve the shape of the target product but also have the features of the style image. The Kansei analysis shows that the semantics of the new product have been enhanced on the basis of the target product, which means that the new product design can better meet the needs of users. Finally, implementation of this proposed method is demonstrated in detail through a case study of female coat design.

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