  The scale and quality of a dataset significantly impact the performance of
deep models. However, acquiring large-scale annotated datasets is both a costly
and time-consuming endeavor. To address this challenge, dataset expansion
technologies aim to automatically augment datasets, unlocking the full
potential of deep models. Current data expansion techniques include image
transformation and image synthesis methods. Transformation-based methods
introduce only local variations, leading to limited diversity. In contrast,
synthesis-based methods generate entirely new content, greatly enhancing
informativeness. However, existing synthesis methods carry the risk of
distribution deviations, potentially degrading model performance with
out-of-distribution samples. In this paper, we propose DistDiff, a
training-free data expansion framework based on the distribution-aware
diffusion model. DistDiff constructs hierarchical prototypes to approximate the
real data distribution, optimizing latent data points within diffusion models
with hierarchical energy guidance. We demonstrate its capability to generate
distribution-consistent samples, significantly improving data expansion tasks.
DistDiff consistently enhances accuracy across a diverse range of datasets
compared to models trained solely on original data. Furthermore, our approach
consistently outperforms existing synthesis-based techniques and demonstrates
compatibility with widely adopted transformation-based augmentation methods.
Additionally, the expanded dataset exhibits robustness across various
architectural frameworks. Our code is available at
https://github.com/haoweiz23/DistDiff
