Title: DeSIGN: Design Inspiration from Generative Networks,
Abstract: 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)
novel loss functions that encourage novelty, inspired from Sharma-Mittal
divergence, a generalized mutual information measure for the widely used
relative entropies such as Kullback-Leibler, and (iii) a generation process
following the key elements of fashion design (disentangling shape and texture
components). 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
criterion yield 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.