  Utilizing auxiliary outlier datasets to regularize the machine learning model
has demonstrated promise for out-of-distribution (OOD) detection and safe
prediction. Due to the labor intensity in data collection and cleaning,
automating outlier data generation has been a long-desired alternative. Despite
the appeal, generating photo-realistic outliers in the high dimensional pixel
space has been an open challenge for the field. To tackle the problem, this
paper proposes a new framework DREAM-OOD, which enables imagining
photo-realistic outliers by way of diffusion models, provided with only the
in-distribution (ID) data and classes. Specifically, DREAM-OOD learns a
text-conditioned latent space based on ID data, and then samples outliers in
the low-likelihood region via the latent, which can be decoded into images by
the diffusion model. Different from prior works, DREAM-OOD enables visualizing
and understanding the imagined outliers, directly in the pixel space. We
conduct comprehensive quantitative and qualitative studies to understand the
efficacy of DREAM-OOD, and show that training with the samples generated by
DREAM-OOD can benefit OOD detection performance. Code is publicly available at
https://github.com/deeplearning-wisc/dream-ood.
