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.
翻译:算法能否创造出独创和令人信服的时装设计来充当灵感助手?为了解答这个问题,我们设计并调查与不同损失功能相关的不同图像生成模型,以提升时尚创作的创造力。我们探索的层面包括:(一) 不同的创性反逆网络结构,从噪音矢量开始,产生时尚物品;(二) 新的损失功能,鼓励创造力;(三) 遵循时尚设计关键要素(不同形状和纹理制造者)的一代过程。本研究的一个主要挑战是评估生成的设计和检索最佳设计,因此,我们制定了一个评价协议,将自动计量和人类实验研究联系起来,我们希望这将有助于缓解未来的研究。我们表明,我们拟议的创造性损失比创意反逆向网络中所使用的结构得到更好的总体赞赏。 最后,我们认为,大约61%的图像是人类设计师创造出来的,而不是计算机创造出来的,同时也被视为人类实验的原创性,我们提出的损失与新颖性和可感性的现有损失相比最高。