The rapid advancement in self-supervised learning (SSL) has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the existing SSL techniques, particularly those employing different augmentations of the same image, often rely on a limited set of simple transformations that are not representative of real-world data variations. This constrains the diversity and quality of samples, which leads to sub-optimal representations. In this paper, we introduce a novel framework that enriches the SSL paradigm by utilizing generative models to produce semantically consistent image augmentations. By directly conditioning generative models on a source image representation, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for self-supervised learning. Our extensive experimental results on various SSL methods demonstrate that our framework significantly enhances the quality of learned visual representations by up to 10\% Top-1 accuracy in downstream tasks. This research demonstrates that incorporating generative models into the SSL workflow opens new avenues for exploring the potential of synthetic data. This development paves the way for more robust and versatile representation learning techniques.

翻译：自监督学习（SSL）的快速发展凸显了其利用未标记数据学习丰富视觉表征的潜力。然而，现有的SSL技术，特别是那些采用同一图像不同增强版本的方法，通常依赖于一组有限的简单变换，这些变换并不能代表真实世界的数据变化。这限制了样本的多样性和质量，从而导致次优的表征。本文提出了一种新颖的框架，通过利用生成模型来产生语义一致的图像增强，从而丰富了SSL范式。我们的方法通过直接以源图像表征为条件来驱动生成模型，能够在保持源图像语义的同时生成多样化的增强样本，从而为自监督学习提供更丰富的数据集。我们在多种SSL方法上进行的广泛实验结果表明，我们的框架将下游任务中的Top-1准确率最高提升了10%，显著提高了所学视觉表征的质量。这项研究表明，将生成模型融入SSL工作流程为探索合成数据的潜力开辟了新途径。这一进展为开发更鲁棒、更通用的表征学习技术铺平了道路。