Balancing Act: Distribution-Guided Debiasing in Diffusion Models

arXiv:2402.18206v1 Announce Type: new
Abstract: Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However, DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces, where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work, we present a method for debiasing DMs without relying on additional data or model retraining. …

abstract act applications arxiv augmentation balancing act biases capability context creative cs.cv data datasets diffusion diffusion models distribution generative generative models image image generation training training datasets type