# Deep Image Clustering Based on Label Similarity and Maximizing Mutual Information across Views
## 1 Introduction  
Clustering is one of the basic tasks in machine learning, computer vision and other fields. It aims to assign unlabeled samples to different clusters so that samples in the same cluster are similar to each other while samples in different clusters are as dissimilar as possible [1]. With the development of deep learning, the need for large-scale labeled datasets is a major obstacle to the application of deep learning in many scenarios. However, manually labeling these datasets is costly and time-consuming. To make better use of unlabeled data, unsupervised clustering has gradually attracted widespread attention. The main research directions of clustering at this stage can be divided into image, text [2], multi-view clustering [3,4] and so on. Among these data, the simplest and most intuitive feeling is image data. Traditional clustering methods, such as k-means [5], Gaussian mixture model [6], density peak clustering [7] and other methods are limited when they meet high-dimensional data, such as images. The reason is the failure of their similarity measures. With the help of fuzzy set theory and its extensions, such as intuitionistic fuzzy sets [8] and others, many novel similarity measures have emerged and made some progress compared to conventional similarity measures. However, a more intuitive solution attracts attention. These methods [9,10] map high-dimensional data into a low-dimensional representation space and perform clustering in the low-dimensional space. These low-dimensional representations are not informative enough, resulting in no significant improvement in the clustering performance. Therefore, image clustering is still a challenging task. Deep learning has been gradually combined with computer vision. Due to the powerful representation extraction ability of deep neural networks, it has achieved successin the fields of classification [11; 12], object recognition [13; 14; 15] and image retrieval [16]. Many scholars turn their attention to deep clustering [17]. Deep clustering refers to the process of automatically learning the data representations and guiding clustering through neural networks. Widely used network architecture includes Convolutional Neural Networks [12; 18], Autoencoders [19] and Variational Autoencoders [20]. Following the success of Generative Adversarial Networks [21], many works [22; 23; 24; 25] introduced the idea of GANs into representation learning and achieved good results. Combining neural networks with traditional clustering algorithms is an effective solution, which consists of two stages. In the first stage, the neural network captures the low-dimensional representations of the samples. In the second stage, the learned low-dimensional representations are served as the input for traditional clustering methods. Some progress has been made compared to traditional methods. Recently, many unsupervised deep learning methods have been proposed to learn clustering. By introducing data augmentation, most of the latest methods look into deep clustering from the perspective that the original image and its transformation should share similar semantic clustering assignments [26; 27; 28]. These methods have achieved some progress on complex natural image datasets. However, the above methods have some limitations: (1) Since generative networks based on autoencoders require pixel-level reconstruction. It is easy to capture redundant image information, such as background, color and other trivial information; (2) These methods only learn class-level representation. However, the instance-level representation could be quite different even if they are assigned to the same cluster since the softmax function is only sensitive to the maximum value. In other words, class-level representation alone is not enough to describe the differences between images belonging to the same cluster. In order to solve the above limitations, this paper proposes a new image clustering model, DCSM. Based on the class-level mapping block (C-block), DCSM adds an instance-level mapping block (IR-block).