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.

翻译：本研究提出了一种新颖的自监督学习方法，该方法从变分推断的角度审视自监督学习，不仅学习表示，还学习表示的不确定性。自监督学习是一种通过最大化同一图像不同增广视图的表示相似性来学习无标签表示的方法。同时，变分自编码器是一种无监督表示学习方法，它通过变分推断训练概率生成模型。变分自编码器和自监督学习都能在没有标签的情况下学习表示，但两者之间的关系此前尚未被探究。本文阐明了自监督学习与变分推断之间的理论关系。此外，提出了一种名为变分推断SimSiam（VI-SimSiam）的新方法。VI-SimSiam通过将SimSiam与变分推断相结合并定义潜空间分布，能够预测表示的不确定性。实验定性表明，VI-SimSiam通过比较输入图像和预测的不确定性能够学习不确定性。此外，我们还描述了估计不确定性与分类准确性之间的关系。