Recent advances in deep clustering and unsupervised representation learning
are based on the idea that different views of an input image (generated through
data augmentation techniques) must either be closer in the representation
space, or have a similar cluster assignment. Bootstrap Your Own Latent (BYOL)
is one such representation learning algorithm that has achieved
state-of-the-art results in self-supervised image classification on ImageNet
under the linear evaluation protocol. However, the utility of the learnt
features of BYOL to perform clustering is not explored. In this work, we study
the clustering ability of BYOL and observe that features learnt using BYOL may
not be optimal for clustering. We propose a novel consensus clustering based
loss function, and train BYOL with the proposed loss in an end-to-end way that
improves the clustering ability and outperforms similar clustering based
methods on some popular computer vision datasets.

本研究探讨并观察到使用 BYOL 学习得到的特征对于聚类可能不是最佳的，提出了一种新颖的基于一致性聚类的损失函数，通过提出的方法进行训练可以提高聚类能力，并在一些流行的计算机视觉数据集上优于相似的聚类方法。