  In this study, a novel self-supervised learning (SSL) method is proposed,
which considers SSL in terms of variational inference to learn not only
representation but also representation uncertainties. SSL is a method of
learning representations without labels by maximizing the similarity between
image representations of different augmented views of an image. Meanwhile,
variational autoencoder (VAE) is an unsupervised representation learning method
that trains a probabilistic generative model with variational inference. Both
VAE and SSL can learn representations without labels, but their relationship
has not been investigated in the past. Herein, the theoretical relationship
between SSL and variational inference has been clarified. Furthermore, a novel
method, namely variational inference SimSiam (VI-SimSiam), has been proposed.
VI-SimSiam can predict the representation uncertainty by interpreting SimSiam
with variational inference and defining the latent space distribution. The
present experiments qualitatively show that VI- SimSiam could learn uncertainty
by comparing input images and predicted uncertainties. Additionally, we
described a relationship between estimated uncertainty and classification
accuracy.
