  Deep clustering against self-supervised learning is a very important and
promising direction for unsupervised visual representation learning since it
requires little domain knowledge to design pretext tasks. However, the key
component, embedding clustering, limits its extension to the extremely
large-scale dataset due to its prerequisite to save the global latent embedding
of the entire dataset. In this work, we aim to make this framework more simple
and elegant without performance decline. We propose an unsupervised image
classification framework without using embedding clustering, which is very
similar to standard supervised training manner. For detailed interpretation, we
further analyze its relation with deep clustering and contrastive learning.
Extensive experiments on ImageNet dataset have been conducted to prove the
effectiveness of our method. Furthermore, the experiments on transfer learning
benchmarks have verified its generalization to other downstream tasks,
including multi-label image classification, object detection, semantic
segmentation and few-shot image classification.
