Design of Semantic-Based Colorization of Graphical User Interface Through Conditional Generative Adversarial Nets

Lee, Younghoon; Cho, Sungzoon

doi:10.1080/10447318.2019.1680921

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…Figure 10 shows the structure of GAN. We summarize the contribution of GAN to product design as follows: schemes generation [151,[178][179][180], text-to-image synthesis [181], generative transformation [182], collocation generation [183][184][185], sketch acquisition [152,186], colorization [187][188][189][190], and virtual display [28,191,192]. Some examples of new product generation based on image data are shown in Figure 11.…”

Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-Driven Product Design: A Survey

Quan,

Li,

Zeng

et al. 2023

Applied Sciences

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As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

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Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-Driven Product Design: A Survey

Quan,

Li,

Zeng

et al. 2023

Applied Sciences

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show abstract

“…If the predicted result is correct, the error will be transferred to G for improvement; otherwise, it will transfer to D for improvement. Eventually, G could capture the statistical distribution of x, and G(z) can deceive D. G(z) is the generated design scheme that contains product features and is different from the real product.We summarize the contribution of GAN to product design as follows: schemes generation [157,[184][185][186], text-to-image synthesis [187], generative transformation [188], collocation generation [189][190][191], sketch acquisition [158,192], colorization [193][194][195][196], and virtual display [28,197,198]. Some examples of new product generation based on image data are shown in Fig.…”

Section: Spark Creative Inspirationmentioning

confidence: 99%

Big Data and AI-driven Product Design: A Survey

Quan

Zeng

et al. 2023

Preprint

View full text Add to dashboard Cite

As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI driven product design methodology with significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review on big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited in aiding product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design.

show abstract

“…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%

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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Design of Semantic-Based Colorization of Graphical User Interface Through Conditional Generative Adversarial Nets

Cited by 8 publications

References 16 publications

Big Data and AI-Driven Product Design: A Survey

Big Data and AI-Driven Product Design: A Survey

Big Data and AI-driven Product Design: A Survey

Big Data driven Product Design: A Survey

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