  A great interest has arisen in using Deep Generative Models (DGM) for
generative design. When assessing the quality of the generated designs, human
designers focus more on structural plausibility, e.g., no missing component,
rather than visual artifacts, e.g., noises in the images. Meanwhile, commonly
used metrics such as Fr\'echet Inception Distance (FID) may not evaluate
accurately as they tend to penalize visual artifacts instead of structural
implausibility. As such, FID might not be suitable to assess the performance of
DGMs for a generative design task. In this work, we propose to encode the input
designs with a simple Denoising Autoencoder (DAE) and measure the distribution
distance in the latent space thereof. We experimentally test our DAE-based
metrics with FID and other state-of-the-art metrics on three data sets:
compared to FID and some more recent works, e.g., FD$_\text{DINO-V2}$ and
topology distance, DAE-based metrics can effectively detect implausible
structures and are more consistent with structural inspection by human experts.
