arXiv:2507.09052v3 Announce Type: replace-cross Abstract: Training data for class-conditional image synthesis often exhibit a long-tailed distribution with limited amount of images for tail classes. Such an imbalance causes mode collapse and reduces the diversity of synthesized images for tail classes. For class-conditional diffusion models trained with imbalanced data, we aim to improve the diversity and fidelity of tail class images without compromising the quality of head class images. We propose contrastive conditional-unconditional alignment (CCUA), which comprises two synergistic loss fu
This development emerges as the field of AI, particularly diffusion models, grapples with real-world data imperfections and biases, making effective handling of long-tailed distributions critical for robust application.
Improved techniques for handling long-tailed data in diffusion models directly impact the quality and diversity of AI-generated content, expanding the utility of these models across various applications while mitigating biases.
The ability to generate high-quality images for less represented categories will improve, leading to more equitable and effective AI applications in image synthesis without sacrificing the performance of well-represented classes.
- · AI developers
- · Creative industries
- · Data scientists
- · AI-powered content platforms
- · Platforms reliant on biased image synthesis
Increased practical deployment of sophisticated generative AI models due to enhanced output quality and diversity.
Reduced need for extensive manual data balancing or augmentation efforts for AI training, streamlining development.
More personalized and niche AI-generated content flourishing, creating new market segments and creative possibilities.
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Read at arXiv cs.LG