Clustering is one of the most fundamental tasks in machine learning.
Recently, deep clustering has become a major trend in clustering techniques.
Representation learning often plays an important role in the effectiveness of
deep clustering, and thus can be a principal cause of performance degradation.
In this paper, we propose a clustering-friendly representation learning method
using instance discrimination and feature decorrelation. Our
deep-learning-based representation learning method is motivated by the
properties of classical spectral clustering. Instance discrimination learns
similarities among data and feature decorrelation removes redundant correlation
among features. We utilize an instance discrimination method in which learning
individual instance classes leads to learning similarity among instances.
Through detailed experiments and examination, we show that the approach can be
adapted to learning a latent space for clustering. We design novel
softmax-formulated decorrelation constraints for learning. In evaluations of
image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy
of 81.5% and 95.4%, respectively. We also show that the softmax-formulated
constraints are compatible with various neural networks.

本研究提出一种基于深度学习的表示学习方法，采用实例区分和特征装饰来提高聚类的效果，在 CIFAR-10 和 ImageNet-10 数据集中进行了实验，分别获得了 81.5% 和 95.4% 的准确率。