DreamDA: Generative Data Augmentation with Diffusion Models

TL;DR

DreamDA leverages diffusion models for data augmentation in classification, generating diverse, high-quality images with accurate labels, leading to improved classifier performance across multiple datasets.

Contribution

This paper introduces DreamDA, a novel diffusion-based data augmentation framework that enhances diversity and label accuracy for classification tasks.

Findings

Consistent performance improvements across four tasks and five datasets.

Effective generation of diverse, high-quality images with correct labels.

Outperforms strong baseline methods in classification accuracy.

Abstract

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.