When Preferences Diverge: Aligning Diffusion Models with Minority-Aware
  Adaptive DPO
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.
67e0bb1665e294ad989334ea
Lingfan Zhang
67e0bb1665e294ad989334eb
Chen Liu
67e0bb1665e294ad989334ec
Chengming Xu
admin_assigned
2025-03-24T12:49:08.734Z
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Chengming Xu
user
ChengmingX
67e0bb1665e294ad989334ed
Kai Hu
67e0bb1665e294ad989334ee
Donghao Luo
67e0bb1665e294ad989334ef
Chengjie Wang
admin_assigned
2025-03-24T12:48:42.939Z
65729087379284b904a6d81d
/avatars/0e7968b1d9221d523a54e3ac787e449d.svg
Chengjie Wang
user
chengjie-wang
67e0bb1665e294ad989334f0
Yanwei Fu
67e0bb1665e294ad989334f1
Yuan Yao
admin_assigned
2025-03-24T12:48:12.962Z
664618d83a7681520570b58a
/avatars/c03e25ee9bbf3504cdf2959acf81e0fe.svg
Yuan Yao
user
yuanyao
When Preferences Diverge: Aligning Diffusion Models with Minority-Aware
  Adaptive DPO
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.
A novel approach, Adaptive-DPO, enhances the training of diffusion models by incorporating a minority-instance-aware metric to effectively manage both synthetic and real-world preference data.
diffusion models Diffusion-DPO universal human preferences preference data minority samples intra-annotator confidence inter-annotator stability Adaptive-DPO synthetic minority data