Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream performance. To overcome this, we propose SASSL: Style Augmentations for Self Supervised Learning, a novel augmentation technique based on Neural Style Transfer. SASSL decouples semantic and stylistic attributes in images and applies transformations exclusively to the style while preserving content, generating diverse samples that better retain semantics. Our technique boosts top-1 classification accuracy on ImageNet by up to 2$\%$ compared to established self-supervised methods like MoCo, SimCLR, and BYOL, while achieving superior transfer learning performance across various datasets.

翻译：现有自监督学习中的数据增强方法虽具有多样性，却未能保持自然图像的内在结构，导致增强样本失真且语义信息受损，最终影响下游任务性能。为解决此问题，我们提出SASSL：面向自监督学习的风格增强技术——一种基于神经风格变换的新型增强方法。SASSL将图像中的语义与风格属性解耦，在保留内容的前提下仅对风格进行变换，从而生成能更好保持语义的多样化样本。相较于MoCo、SimCLR和BYOL等成熟自监督方法，该技术在ImageNet上的top-1分类准确率提升高达2%，并在多个数据集上取得更优的迁移学习性能。