Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design. (arXiv:2206.07170v1 [cs.LG])

Web: http://arxiv.org/abs/2206.07170

Deep Generative Machine Learning Models (DGMs) have been growing in
popularity across the design community thanks to their ability to learn and
mimic complex data distributions. DGMs are conventionally trained to minimize
statistical divergence between the distribution over generated data and
distribution over the dataset on which they are trained. While sufficient for
the task of generating "realistic" fake data, this objective is typically
insufficient for design synthesis tasks. Instead, design problems typically
call for adherence to design requirements, such …

arxiv deep deep generative models design diversity lg models