  We present Self-Classifier -- a novel self-supervised end-to-end
classification neural network. Self-Classifier learns labels and
representations simultaneously in a single-stage end-to-end manner by
optimizing for same-class prediction of two augmented views of the same sample.
To guarantee non-degenerate solutions (i.e., solutions where all labels are
assigned to the same class), a uniform prior is asserted on the labels. We show
mathematically that unlike the regular cross-entropy loss, our approach avoids
such solutions. Self-Classifier is simple to implement and is scalable to
practically unlimited amounts of data. Unlike other unsupervised classification
approaches, it does not require any form of pre-training or the use of
expectation maximization algorithms, pseudo-labelling or external clustering.
Unlike other contrastive learning representation learning approaches, it does
not require a memory bank or a second network. Despite its relative simplicity,
our approach achieves comparable results to state-of-the-art performance with
ImageNet, CIFAR10 and CIFAR100 for its two objectives: unsupervised
classification and unsupervised representation learning. Furthermore, it is the
first unsupervised end-to-end classification network to perform well on the
large-scale ImageNet dataset. Code will be made available.
