标题： DeepVAT：一种用于图像数据集聚类评估的自监督技术 显示英文标题

标题： DeepVAT: A Self-Supervised Technique for Cluster Assessment in Image Datasets

摘要： 估计无标签、复杂且高维数据集（如图像）中的聚类数量和聚类结构对传统聚类算法来说是一个挑战。 近年来，一种基于矩阵重新排序的算法称为视觉评估趋势（VAT），以及其变体吸引了来自不同领域的许多研究人员，以估计数据中存在的聚类数量和固有聚类结构。 然而，这些算法在处理图像数据时面临重大挑战，因为它们无法有效捕捉图像中固有的关键特征。 为了克服这些限制，我们提出了一种基于深度学习的框架，以实现对复杂图像数据集的聚类结构评估。 我们的方法利用自监督深度神经网络生成数据的代表性嵌入。 这些嵌入随后通过t分布随机邻域嵌入（t-SNE）降维到2维，并输入到基于VAT的算法中以估计潜在的聚类结构。 重要的是，我们的框架不依赖于聚类数量的先验知识。 我们在四个基准图像数据集（即MNIST、FMNIST、CIFAR-10和INTEL）上，与最先进的VAT家族算法以及其他两种深度聚类算法相比，展示了更优越的性能。 显示英文摘要

摘要： Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms. In recent years, a matrix reordering-based algorithm called Visual Assessment of Tendency (VAT), and its variants have attracted many researchers from various domains to estimate the number of clusters and inherent cluster structure present in the data. However, these algorithms face significant challenges when dealing with image data as they fail to effectively capture the crucial features inherent in images. To overcome these limitations, we propose a deep-learning-based framework that enables the assessment of cluster structure in complex image datasets. Our approach utilizes a self-supervised deep neural network to generate representative embeddings for the data. These embeddings are then reduced to 2-dimension using t-distributed Stochastic Neighbour Embedding (t-SNE) and inputted into VAT based algorithms to estimate the underlying cluster structure. Importantly, our framework does not rely on any prior knowledge of the number of clusters. Our proposed approach demonstrates superior performance compared to state-of-the-art VAT family algorithms and two other deep clustering algorithms on four benchmark image datasets, namely MNIST, FMNIST, CIFAR-10, and INTEL.