Dream the Impossible: Outlier Imagination with Diffusion Models
This addresses the problem of automating outlier data generation for machine learning practitioners, reducing reliance on labor-intensive data collection, though it is an incremental advance in a specific domain.
The paper tackles the challenge of generating photo-realistic outliers for out-of-distribution detection by proposing DREAM-OOD, a framework that uses diffusion models to imagine such outliers from in-distribution data, and shows that training with these generated samples improves OOD detection performance.
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.