Digital design of regional characteristic apparel pattern driven by GAN

In the links of apparel product development and production, apparel pattern design cannot reduce its marginal cost through economies of scale because of its creative characteristics. With the world entering the era of industry 4.0, machine learning can provide services for apparel design. This resea...

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Vydané v:Industria textilă (Bucharest, Romania : 1994) Ročník 73; číslo 3; s. 233 - 240
Hlavní autori: CHEN, HAN, SHEN, LEI, ZHANG, XIYING, REN, XIANGFANG, WANG, MINGMING, MIN, XUE, LI, XUE
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bucharest The National Research & Development Institute for Textiles and Leather - INCDTP 01.01.2022
Predmet:
Algorithms
Artistic expression
Clothing industry
Convergence
Design techniques
Economies of scale
Experiments
Generative adversarial networks
Machine learning
Methods
Modules
Product design
Product development
Regional development
Training
Visual perception
ISSN:1222-5347
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Abstract In the links of apparel product development and production, apparel pattern design cannot reduce its marginal cost through economies of scale because of its creative characteristics. With the world entering the era of industry 4.0, machine learning can provide services for apparel design. This research takes the Chinese characteristic tachisme pattern as the research object and puts forward a new design method of regional characteristic apparel pattern driven by Generative Adversarial Networks (GAN). Firstly, the main framework based on GAN including discrimination and generation modules is established. Aiming at the training difficulties of regional characteristic apparel pattern sample situation with small quantity and disordered specification, the image self-amplification and normalization pre-processing module is added to the model. Secondly, by adding the Batch Normalization mechanism, Leaky ReLU and RMSProp algorithm, the problems of gradient disappearance and overfitting in the experiment are solved, and the convergence speed of the model is improved. Finally, the HSV colour model algorithm is introduced into the loss function to indicate the training process, so that the artistic expression characteristics of the generated results are closer to the human visual perception experience. Through index evaluation comparison, result authenticity investigation and product design practice, we prove the superiority and practicality of the proposed method in this paper. The new design method theoretically solves the scale economy dilemma of the previous apparel pattern design methods and provides reference ideas for more application scenarios currently trapped in the real-time presentation of design results.
AbstractList In the links of apparel product development and production, apparel pattern design cannot reduce its marginal cost through economies of scale because of its creative characteristics. With the world entering the era of industry 4.0, machine learning can provide services for apparel design. This research takes the Chinese characteristic tachisme pattern as the research object and puts forward a new design method of regional characteristic apparel pattern driven by Generative Adversarial Networks (GAN). Firstly, the main framework based on GAN including discrimination and generation modules is established. Aiming at the training difficulties of regional characteristic apparel pattern sample situation with small quantity and disordered specification, the image self-amplification and normalization pre-processing module is added to the model. Secondly, by adding the Batch Normalization mechanism, Leaky ReLU and RMSProp algorithm, the problems of gradient disappearance and overfitting in the experiment are solved, and the convergence speed of the model is improved. Finally, the HSV colour model algorithm is introduced into the loss function to indicate the training process, so that the artistic expression characteristics of the generated results are closer to the human visual perception experience. Through index evaluation comparison, result authenticity investigation and product design practice, we prove the superiority and practicality of the proposed method in this paper. The new design method theoretically solves the scale economy dilemma of the previous apparel pattern design methods and provides reference ideas for more application scenarios currently trapped in the real-time presentation of design results.
In the links of apparel product development and production, apparel pattern design cannot reduce its marginal cost through economies of scale because of its creative characteristics. With the world entering the era of industry 4.0, machine learning can provide services for apparel design. This research takes the Chinese characteristic tachisme pattern as the research object and puts forward a new design method of regional characteristic apparel pattern driven by Generative Adversarial Networks (GAN). Firstly, the main framework based on GAN including discrimination and generation modules is established. Aiming at the training difficulties of regional characteristic apparel pattern sample situation with small quantity and disordered specification, the image self-amplification and normalization pre-processing module is added to the model. Secondly, by adding the Batch Normalization mechanism, Leaky ReLU and RMSProp algorithm, the problems of gradient disappearance and overfitting in the experiment are solved, and the convergence speed of the model is improved. Finally, the HSV colour model algorithm is introduced into the loss function to indicate the training process, so that the artistic expression characteristics of the generated results are closer to the human visual perception experience. Through index evaluation comparison, result authenticity investigation and product design practice, we prove the superiority and practicality of the proposed method in this paper. The new design method theoretically solves the scale economy dilemma of the previous apparel pattern design methods and provides reference ideas for more application scenarios currently trapped in the real-time presentation of design results.
Author LI, XUE
CHEN, HAN
REN, XIANGFANG
MIN, XUE
SHEN, LEI
WANG, MINGMING
ZHANG, XIYING
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SubjectTerms Algorithms
Artistic expression
Clothing industry
Convergence
Design techniques
Economies of scale
Experiments
Generative adversarial networks
Machine learning
Methods
Modules
Product design
Product development
Regional development
Training
Visual perception
Title Digital design of regional characteristic apparel pattern driven by GAN
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