When Preferences Diverge: Aligning Diffusion Models with Minority-Aware
  Adaptive DPO
Authors: name: Lingfan Zhang, name: Chen Liu, name: Chengming Xu, name: Kai Hu, name: Donghao Luo, name: Chengjie Wang, name: Yanwei Fu, name: Yuan Yao

In recent years, the field of image generation has witnessed significant
advancements, particularly in fine-tuning methods that align models with
universal human preferences. This paper explores the critical role of
preference data in the training process of diffusion models, particularly in
the context of Diffusion-DPO and its subsequent adaptations. We investigate the
complexities surrounding universal human preferences in image generation,
highlighting the subjective nature of these preferences and the challenges
posed by minority samples in preference datasets. Through pilot experiments, we
demonstrate the existence of minority samples and their detrimental effects on
model performance. We propose Adaptive-DPO -- a novel approach that
incorporates a minority-instance-aware metric into the DPO objective. This
metric, which includes intra-annotator confidence and inter-annotator
stability, distinguishes between majority and minority samples. We introduce an
Adaptive-DPO loss function which improves the DPO loss in two ways: enhancing
the model's learning of majority labels while mitigating the negative impact of
minority samples. Our experiments demonstrate that this method effectively
handles both synthetic minority data and real-world preference data, paving the
way for more effective training methodologies in image generation tasks.