  Negative-free contrastive learning methods have attracted a lot of attention
with simplicity and impressive performances for large-scale pretraining.
However, its disentanglement property remains unexplored. In this paper, we
examine negative-free contrastive learning methods to study the disentanglement
property empirically. We find that existing disentanglement metrics fail to
make meaningful measurements for high-dimensional representation models, so we
propose a new disentanglement metric based on Mutual Information between latent
representations and data factors. With this proposed metric, we benchmark the
disentanglement property of negative-free contrastive learning on both popular
synthetic datasets and a real-world dataset CelebA. Our study shows that the
investigated methods can learn a well-disentangled subset of representation. As
far as we know, we are the first to extend the study of disentangled
representation learning to high-dimensional representation space and introduce
negative-free contrastive learning methods into this area. The source code of
this paper is available at
\url{https://github.com/noahcao/disentanglement_lib_med}.
