Creative Intelligence – Automating Car Design Studio with Generative Adversarial Networks (GAN)

Radhakrishnan, Sreedhar; Bharadwaj, Varun; Manjunath, Varun; Srinath, Ramamoorthy

doi:10.1007/978-3-319-99740-7_11

Cited by 19 publications

(12 citation statements)

References 14 publications

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“…Radha Krishnan et al [36] proposed a method for creating unique automobile designs from drawings based on a GAN. The authors created a matched data set of approximately 100,000 automobile pictures (with transparent backgrounds) and corresponding drawings to train and test the algorithms.…”

Section: Related Survey On Gan Applicationsmentioning

confidence: 99%

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

2022

ijcsm

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GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate background signals. GANs can use learned representations for a variety of applications, including image synthesis, semantic imaging, style transfer, super magnification, and segmentation. Images can be utilized in a variety of ways. Generative Adversarial Networks (GANs) are a unique class that has recently received a lot of interest due to the popularity of deep generative models. GANs implicitly distribute complex and high-resolution images, sounds, and data. However, due to inadvertently built network architecture, objective function usage, and optimization algorithm selection, significant difficulties such as mode collapse, inconsistencies, and instability develop while training GANs. In this paper, we conduct a thorough examination of the developments in GANs design and optimization strategies presented to address GANs difficulties. We provide intriguing study possibilities in this rapidly evolving area. GANs are a popular study topic due to their ability to generate synthetic data and the benefits of representations that can be understood regardless of the application. While various reviews for GANs in the image processing arena have been undertaken to date, none have focused on the review of GANs in multi-disciplinary domains. As a result, the utilization of GAN in interdisciplinary applications fields and its implementation issues were investigated in this survey by doing a thorough search for journal/research article connected to GAN.

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Section: Related Survey On Gan Applicationsmentioning

confidence: 99%

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

2022

ijcsm

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“…In recent years, some researchers have tried to apply GANs to product designs. For example, Radhakrishnan [23] proposed a GAN-based intelligent vehicle design model, which created new unseen design schemes through the sketches of a car design studio, thereby improving design efficiency. Kularatne [24] proposed a fashion design method combining the expertise of fashion designers and pattern makers by using a GAN on the basis of existing fashion styles to generate new fashion design schemes using existing clothes.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

et al. 2021

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Currently, evaluations of products from aesthetics are mostly carried out with knowledge expressions of aesthetic features as tools, achieving remarkable results. However, obtaining a large aesthetic feature vocabulary is a challenge because of the experience of researchers and the comprehension abilities of subjects. In addition, due to manual feature extraction, the sample sizes of experimental dataset are generally small, leading to results with poor generalization. To address this problem, a method of aesthetic evaluation and form design for products based on deep learning was proposed. First, a crawler tool was used to collect the front images of cars with corresponding appearance ratings, and a dataset was constructed with users' intuitive and simple ratings as the labels. A deep convolutional neural network (CNN) was designed, and a grading threshold was used as the classification basis. During the process of training the network, batch normalization and other methods were used to optimize the network, and good classification effects were achieved. Based on the above work, an adversarial neural network was used for the aesthetic design of a product form, a shape sketch of an automobile front face was generated, the proposed evaluation model was used to evaluate it, and the result obtained was excellent. These results show that the method used in this study can correctly evaluate product form aesthetics and then generate a design scheme with a high aesthetic level, thereby providing powerful technical support for the intelligent design of product forms.

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“…Figure 10 shows the structure of GAN. We summarized the contribution of GAN to product design as schemes generation [191,192,5], text-to-image synthesis [193], generative transformation [194], collocation generation [195,196,197], sketch acquisition [198,199], colorization [200,201,202], and virtual display [13,203].…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

“…Their results showed that GAN has an excellent ability to learn the color rules of ethnic costumes. Sreedhar et al [5] proposed a car design system based on GAN that supports single-color, dual-color, and multiple-color coloring from a single sketch.…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

“…The design concept is changing from 'form follows function', 'technology first', and 'product-centric' to 'form follows emotion' and 'user-centric' [2]. The design method is changing from experiential design and fuzzy design to intelligent design, computer-aided design, and multi-domain joint design [3,4,5]. At the same time, customers pay more and more attention to the spiritual and emotional requirements, which are diversified and personalized, especially in product appearance [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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Creative Intelligence – Automating Car Design Studio with Generative Adversarial Networks (GAN)

Cited by 19 publications

References 14 publications

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

Big Data driven Product Design: A Survey

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