Contrastive learning is a good way to pursue discriminative unsupervised learning, which can inherit advantages and experiences of well-studied deep models without complexly novel model designing. In this paper, we propose two learning method for document clustering, the one is a partial contrastive learning with unsupervised data augment, and the other is a self-supervised contrastive learning. Both methods achieve state-of-the-art results in clustering accuracy when compared to recently proposed unsupervised clustering approaches.

翻译：反向学习是追求歧视性的、不受监督的学习的好方法,这种学习可以继承研究周全的深层模型的优势和经验,而没有复杂的新颖模型的设计。 在本文中,我们提出了两种文件分类的学习方法:一种是部分对比学习,没有监督的数据增加;另一种是自我监督的反向学习。 这两种方法与最近提出的未经监督的分组方法相比,在组合准确性方面都取得了最先进的成果。