Contrastive Learning (CL) has emerged as one of the most successful paradigms
for unsupervised visual representation learning, yet it often depends on
intensive manual data augmentations. With the rise of generative models,
especially diffusion models, the ability to generate realistic images close to
the real data distribution has been well recognized. These generated
high-equality images have been successfully applied to enhance contrastive
representation learning, a technique termed ``data inflation''. However, we
find that the generated data (even from a good diffusion model like DDPM) may
sometimes even harm contrastive learning. We investigate the causes behind this
failure from the perspective of both data inflation and data augmentation. For
the first time, we reveal the complementary roles that stronger data inflation
should be accompanied by weaker augmentations, and vice versa. We also provide
rigorous theoretical explanations for these phenomena via deriving its
generalization bounds under data inflation. Drawing from these insights, we
propose Adaptive Inflation (AdaInf), a purely data-centric strategy without
introducing any extra computation cost. On benchmark datasets, AdaInf can bring
significant improvements for various contrastive learning methods. Notably,
without using external data, AdaInf obtains 94.70% linear accuracy on CIFAR-10
with SimCLR, setting a new record that surpasses many sophisticated methods.
Code is available at this https URL

逆变学习作为无监督视觉表示学习中最成功的方法之一，依赖于大量的手动数据增强。然而，本文研究发现由扩散模型生成的数据有时会对对比学习产生负面影响，并提出了一种数据为中心的策略，即自适应扩增 (AdaInf)，无需额外的计算成本，可在各种对比学习方法上取得显著的改进。