Fashion-AttGAN: Attribute-Aware Fashion Editing With Multi-Objective GAN

Ping, Qing; Wu, Bing; Ding, Wanying; Yuan, Jiangbo

doi:10.1109/cvprw.2019.00044

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…We perform additional experiments on the fashion dataset [44], which contains 14221 images annotated with 22 binary attributes (colors and sleeve lengths). We select nine attributes owned by most images (black, white, red, grey, navyblue, blue, pink, long-sleeve, no-sleeve) as labels to train ARU-GAN.…”

Section: F Results On Other Datasetsmentioning

confidence: 99%

Adversarially Regularized U-Net-based GANs for Facial Attribute Modification and Generation

Zhang

Liu

et al. 2019

IEEE Access

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Modifying and generating facial images with desired attributes are two important and highly related tasks in the field of computer vision. Some current methods can take advantage of their relationship and use a unified model to handle them simultaneously. However, producing high visual quality images on both tasks is still a challenge. To tackle this issue, we propose a novel model called adversarially regularized U-net (ARU-net)-based generative adversarial networks (ARU-GANs). The ARU-net is the major part of the ARU-GAN and is inspired by the design principle of U-net. It uses skip connections to pass different-level features from encoder to decoder, which preserves sufficient attribute-independent details for the modification task. Besides, this U-net-like architecture employs an adversarial regularization term to guide the distribution of latent representation to match the prior distribution, which guarantees to generate meaningful faces from this prior. We also propose a joint training technique for the ARU-GAN, which enables the facial attribute modification and generation tasks to learn together during training. We perform experiments on celebfaces attributes (CelebA) dataset and make visual analysis and quantitative evaluation on both tasks, which demonstrates that our model can successfully produce high visual quality facial images. Also, the results show that learning two tasks jointly can lead to performance improvement compared with learning them individually. At last, we further validate the effectiveness of our method by making an ablation study and experimenting on another dataset. INDEX TERMS Generative adversarial networks, facial images, skip connections, adversarial regularization.

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Section: F Results On Other Datasetsmentioning

confidence: 99%

Adversarially Regularized U-Net-based GANs for Facial Attribute Modification and Generation

Zhang

Liu

et al. 2019

IEEE Access

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“…GAN is not a generic, domain-agnostic deep learning technique to be directly utilized in industrial applications such as generative design. As illustrated in the experiments, although AttGAN performs exceptionally well on the facial attribute editing task, it fails to carry out the same task on Figure 6: Attribute editing accuracy comparison among DA-GAN, Fashion-AttGAN [62], and AttGAN [27]. a different dataset from the fashion domain.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

“…The majority of the attribute-aware image generation methods summarized above have been designed around facial attribute editing without any indication or proof of their applicability to other domains such as fashion product design. Fashion-AttGAN [62] introduces an attribute-aware fashion editing model based on AttGAN model. However, their attributes are limited to the color and sleeve length.…”

Section: B Attribute-aware Ganmentioning

confidence: 99%

Attribute-Aware Generative Design With Generative Adversarial Networks

Yuan

Moghaddam

2020

IEEE Access

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The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process. The shrinking timeto-market and the growing diversity of users' needs further exacerbate this gap. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation or editing of design concepts. This paper explores the capabilities of generative adversarial networks (GAN) for automated, attribute-aware generative design of the visual attributes of a product. Specifically, a design attribute GAN (DA-GAN) model is developed for automated generation of fashion product images with the desired visual attributes. Experiments on a large fashion dataset signify the potentials of GAN for attribute-aware generative design, verify the ability of editing attributes with relatively higher accuracy and uncover several key challenges and research questions for future work.

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“…The Design-AttGAN model is tested on a fashion dataset [31], which contains 13221 images and each of which has annotation of 22 binary attributes (with/without). Attributes with great frequency are chosen in all our experiments, including "vest", "polo", "stripe", "short sleeve", "long sleeve", "red", "yellow", "blue", "purple", "black", and "white".…”

Section: Experiments 41 Dataset and Trainingmentioning

confidence: 99%

Garment Design with Generative Adversarial Networks

Yuan,

Moghaddam

2020

Preprint

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The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often hinder their ability to innovate during the design thinking and ideation process. In the fashion industry, in particular, the growing diversity of customers' needs, the intense global competition, and the shrinking time-to-market (a.k.a., "fast fashion") further exacerbate this challenge for designers. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation and/or editing of design concepts. This paper explores the capabilities of generative adversarial networks (GAN) for automated attribute-level editing of design concepts. Specifically, attribute GAN (AttGAN)-a generative model proven successful for attribute editing of human faces-is utilized for automated editing of the visual attributes of garments and tested on a large fashion dataset. The experiments support the hypothesized potentials of GAN for attribute-level editing of design concepts, and underscore several key limitations and research questions to be addressed in future work. CCS CONCEPTS• Computing methodologies → Artificial intelligence.

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Fashion-AttGAN: Attribute-Aware Fashion Editing With Multi-Objective GAN

Cited by 17 publications

References 10 publications

Adversarially Regularized U-Net-based GANs for Facial Attribute Modification and Generation

Adversarially Regularized U-Net-based GANs for Facial Attribute Modification and Generation

Attribute-Aware Generative Design With Generative Adversarial Networks

Garment Design with Generative Adversarial Networks

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