Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) a new loss function that encourages creativity, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture makers). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity loss yields better overall appreciation than the one employed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.

本研究探讨了结合不同损失函数的图像生成模型在时尚生成中的创新性。通过设计一个评估协议，结合自动度量和人类实验研究，本研究发现，相对于Creative Adversarial Networks所采用的创新性评估标准，本研究所提出的创新性标准更具创意性。以及在新颖性和喜好性方面，本研究所提出的损失函数得分最高。

来自生成网络的设计启发