DDE-GAN: Integrating a Data-driven Design Evaluator into Generative Adversarial Networks for Desirable and Diverse Concept Generation
DDE-GAN: Integrating a Data-driven Design Evaluator into Generative Adversarial Networks for Desirable and Diverse Concept Generation
复制标题
DDE-GAN：将数据驱动的设计评估器集成到生成对抗网络中，以生成理想且多样化的概念
DOI：
10.1115/1.4056500
复制
发表时间：
2022
影响因子：
3.3
通讯作者：
Moghaddam, Mohsen
中科院分区：
文献类型：
--
作者：
Yuan, Chenxi;Marion, Tucker;Moghaddam, Mohsen
生成对抗网络（GAN）在各种生成设计任务中取得了显着的成功，从拓扑优化到材料设计和形状参数化。然而，大多数基于 GAN 的生成设计方法缺乏评估机制来确保生成多样化的样本。此外，基于 GAN 的生成设计模型还没有将用户情绪纳入损失函数中，从而从用户的总体角度生成具有高合意性的样本。在这些知识差距的推动下，本文构建并验证了一种新颖的基于 GAN 的生成设计模型，该模型具有离线设计评估功能，以生成不仅现实而且多样化且理想的样本。开发了多模态数据驱动设计评估（DDE）模型，通过根据对先前设计的大规模用户评论自动预测生成样本的用户情绪来指导生成过程。本文将 DDE 融入到最先进的 GAN 模型 StyleGAN 结构中，以实现创新且以用户为中心的数据驱动生成过程。在大型鞋类产品数据集上进行的实验结果证明了所提出的 DDE-GAN 在生成高质量、多样化和理想概念方面的有效性。
Generative adversarial networks (GANs) have shown remarkable success in various generative design tasks, from topology optimization to material design, and shape parametrization. However, most generative design approaches based on GANs lack evaluation mechanisms to ensure the generation of diverse samples. In addition, no GAN-based generative design model incorporates user sentiments in the loss function to generate samples with high desirability from the aggregate perspectives of users. Motivated by these knowledge gaps, this paper builds and validates a novel GAN-based generative design model with an offline design evaluation function to generate samples that are not only realistic but also diverse and desirable. A multimodal data-driven design evaluation (DDE) model is developed to guide the generative process by automatically predicting user sentiments for the generated samples based on large-scale user reviews of previous designs. This paper incorporates DDE into the StyleGAN structure, a state-of-the-art GAN model, to enable data-driven generative processes that are innovative and user-centered. The results of experiments conducted on a large dataset of footwear products demonstrate the effectiveness of the proposed DDE-GAN in generating high-quality, diverse, and desirable concepts.