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 methods encompass image
transformation-based and synthesis-based methods. The transformation-based
methods introduce only local variations, resulting in poor diversity. While
image synthesis-based methods can create entirely new content, significantly
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, an effective
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 ability to generate
distribution-consistent samples, achieving substantial improvements in data
expansion tasks. Specifically, without additional training, DistDiff achieves a
30.7% improvement in accuracy across six image datasets compared to the model
trained on original datasets and a 9.8% improvement compared to the
state-of-the-art diffusion-based method. Our code is available at
this https URL

DistDiff 是一种基于分布感知扩散模型的有效数据扩充框架，通过构建分层原型来逼近真实数据分布，优化扩散模型中的潜在数据点，实现生成与目标分布一致的样本，从而在数据扩充任务中取得显著的改进。