  Denoising diffusion probabilistic models (DDPMs) are a recent family of
generative models that achieve state-of-the-art results. In order to obtain
class-conditional generation, it was suggested to guide the diffusion process
by gradients from a time-dependent classifier. While the idea is theoretically
sound, deep learning-based classifiers are infamously susceptible to
gradient-based adversarial attacks. Therefore, while traditional classifiers
may achieve good accuracy scores, their gradients are possibly unreliable and
might hinder the improvement of the generation results. Recent work discovered
that adversarially robust classifiers exhibit gradients that are aligned with
human perception, and these could better guide a generative process towards
semantically meaningful images. We utilize this observation by defining and
training a time-dependent adversarially robust classifier and use it as
guidance for a generative diffusion model. In experiments on the highly
challenging and diverse ImageNet dataset, our scheme introduces significantly
more intelligible intermediate gradients, better alignment with theoretical
findings, as well as improved generation results under several evaluation
metrics. Furthermore, we conduct an opinion survey whose findings indicate that
human raters prefer our method's results.
