LTB-Solver: Long-tailed Bias Solver for image synthesis of diffusion models

• Published in: Neurocomputing (Amsterdam) Vol. 634; p. 129651
• Main Authors: Fu, Siming, He, Xiaoxuan, Hu, Haoji
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 14.06.2025
• Subjects: Diffusion models; Long-tailed learning; Diffusion models; Long-tailed learning
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2025.129651

Though diffusion models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies, they do not generalize well on long-tailed datasets due to the minority classes lacking of diversity and semantic information. To overcome the aforementioned challenges, we first take a closer look at the collapse of tail category patterns under long-tail distributed data and propose an alternative but easy-to-use and effective solution, a Long-Tailed Bias Solver in diffusion model image synthesis (LTB-Solver), which thereby enhances the overall diversity and quality of synthetic samples building upon the properties of the long-tailed distribution training data. Especially, we extract rich generative distribution knowledge of ‘head’ categories within proxy model and transfer the head-tail consistency distance to ‘tail’ categories, enabling the target diffusion model to learn diverse generation preserving inter-sample variation during the diffusion training process. Moreover, we incorporate the minority guidance loss function that better aligns training objectives with sampling behaviors and adjust the loss values for different classes by multiplying them with different weights. Extensive experiments are conducted on various datasets and several state-of-the-art diffusion model frameworks to verify the effectiveness of the proposed method. The results show that our method significantly improves the performance of diffusion models on long-tailed datasets by a large margin.