Unsupervised contrastive learning methods have recently seen significant
improvements, particularly through data augmentation strategies that aim to
produce robust and generalizable representations. However, prevailing data
augmentation methods, whether hand designed or based on foundation models, tend
to rely heavily on prior knowledge or external data. This dependence often
compromises their effectiveness and efficiency. Furthermore, the applicability
of most existing data augmentation strategies is limited when transitioning to
other research domains, especially science-related data. This limitation stems
from the paucity of prior knowledge and labeled data available in these
domains. To address these challenges, we introduce DiffAug-a novel and
efficient Diffusion-based data Augmentation technique. DiffAug aims to ensure
that the augmented and original data share a smoothed latent space, which is
achieved through diffusion steps. Uniquely, unlike traditional methods, DiffAug
first mines sufficient prior semantic knowledge about the neighborhood. This
provides a constraint to guide the diffusion steps, eliminating the need for
labels, external data/models, or prior knowledge. Designed as an
architecture-agnostic framework, DiffAug provides consistent improvements.
Specifically, it improves image classification and clustering accuracy by
1.6%~4.5%. When applied to biological data, DiffAug improves performance by up
to 10.1%, with an average improvement of 5.8%. DiffAug shows good performance
in both vision and biological domains.

通过扩散步骤，DiffAug 引入了一种新颖且高效的基于扩散的数据增强技术，以确保增强数据和原始数据在光滑的潜在空间中共享，从而通过无需标签、外部数据 / 模型或先前知识的约束来提高图像分类和聚类准确性，可适用于视觉和生物领域。