DreamDA: Generative Data Augmentation with Diffusion Models
Yunxiang Fu Chaoqi Chen Yu Qiao Yizhou Yu
The acquisition of large-scale, high-quality data is a resource-intensive and
time-consuming endeavor. Compared to conventional Data Augmentation (DA)
techniques (e.g. cropping and rotation), exploiting prevailing diffusion models
for data generation has received scant attention in classification tasks.
Existing generative DA methods either inadequately bridge the domain gap
between real-world and synthesized images, or inherently suffer from a lack of
diversity. To solve these issues, this paper proposes a new
classification-oriented framework DreamDA, which enables data synthesis and
label generation by way of diffusion models. DreamDA generates diverse samples
that adhere to the original data distribution by considering training images in
the original data as seeds and perturbing their reverse diffusion process. In
addition, since the labels of the generated data may not align with the labels
of their corresponding seed images, we introduce a self-training paradigm for
generating pseudo labels and training classifiers using the synthesized data.
Extensive experiments across four tasks and five datasets demonstrate
consistent improvements over strong baselines, revealing the efficacy of
DreamDA in synthesizing high-quality and diverse images with accurate labels.
Our code will be available at https://github.com/yunxiangfu2001/DreamDA.