  In contrastive learning in the image domain, the anchor and positive samples
are forced to have as close representations as possible. However, forcing the
two samples to have the same representation could be misleading because the
data augmentation techniques make the two samples different. In this paper, we
introduce a new representation, partitioned representation, which can learn
both common and unique features of the anchor and positive samples in
contrastive learning. The partitioned representation consists of two parts: the
content part and the style part. The content part represents common features of
the class, and the style part represents the own features of each sample, which
can lead to the representation of the data augmentation method. We can achieve
the partitioned representation simply by decomposing a loss function of
contrastive learning into two terms on the two separate representations,
respectively. To evaluate our representation with two parts, we take two
framework models: Variational AutoEncoder (VAE) and BootstrapYour Own
Latent(BYOL) to show the separability of content and style, and to confirm the
generalization ability in classification, respectively. Based on the
experiments, we show that our approach can separate two types of information in
the VAE framework and outperforms the conventional BYOL in linear separability
and a few-shot learning task as downstream tasks.
