  Data augmentation is a crucial component in unsupervised contrastive learning
(CL). It determines how positive samples are defined and, ultimately, the
quality of the learned representation. In this work, we open the door to new
perspectives for CL by integrating prior knowledge, given either by generative
models -- viewed as prior representations -- or weak attributes in the positive
and negative sampling. To this end, we use kernel theory to propose a novel
loss, called decoupled uniformity, that i) allows the integration of prior
knowledge and ii) removes the negative-positive coupling in the original
InfoNCE loss. We draw a connection between contrastive learning and conditional
mean embedding theory to derive tight bounds on the downstream classification
loss. In an unsupervised setting, we empirically demonstrate that CL benefits
from generative models to improve its representation both on natural and
medical images. In a weakly supervised scenario, our framework outperforms
other unconditional and conditional CL approaches.
