Dataset bias is a significant challenge in machine learning, where specific attributes, such as texture or color of the images are unintentionally learned resulting in detrimental performance. To address this, previous efforts have focused on debiasing models either by developing novel debiasing algorithms or by generating synthetic data to mitigate the prevalent dataset biases. However, generative approaches to date have largely relied on using bias-specific samples from the dataset, which are typically too scarce. In this work, we propose, DiffInject, a straightforward yet powerful method to augment synthetic bias-conflict samples using a pretrained diffusion model. This approach significantly advances the use of diffusion models for debiasing purposes by manipulating the latent space. Our framework does not require any explicit knowledge of the bias types or labelling, making it a fully unsupervised setting for debiasing. Our methodology demonstrates substantial result in effectively reducing dataset bias.

翻译：数据集偏差是机器学习中的一项重大挑战，其中图像特定属性（如纹理或颜色）被无意学习，导致性能受损。为解决这一问题，先前的研究主要集中在通过开发新型去偏算法或生成合成数据来减轻普遍存在的数据集偏差。然而，迄今为止的生成方法大多依赖于使用数据集中通常极为稀缺的偏差特定样本。在本工作中，我们提出DiffInject——一种利用预训练扩散模型增强合成偏差冲突样本的简洁而强大的方法。该方法通过操控潜在空间，显著推进了扩散模型在去偏任务中的应用。我们的框架无需明确知晓偏差类型或标注信息，实现了完全无监督的去偏设定。实验证明，该方法在有效减少数据集偏差方面取得了实质性成果。