论文标题
DEEPDPM：深集聚类，簇数量未知
DeepDPM: Deep Clustering With an Unknown Number of Clusters
论文作者
论文摘要
深度学习（DL）在无监督的聚类任务中表现出了巨大的希望。也就是说，尽管在古典（即非深）中，非参数方法的好处是众所周知的，但大多数深层聚类方法都是参数：即，它们需要一个预定义的固定数量和固定数量的群集，而K。当K使用模型的计算值却是众多的计算，尤其是在计算中，尤其是在多个过程中，尤其是在多个过程中，尤其是在多个过程中，尤其是在多个训练中，尤其是在多个过程中，尤其是在多个过程中均可进行训练。在这项工作中，我们通过引入一种有效的深层聚类方法来弥合这一差距，该方法不需要在学习过程中了解k的价值。使用拆分/合并框架，适应不断变化的K的动态体系结构以及新的损失，我们提出的方法的表现优于现有的非参数方法（无论是经典还是深度）。尽管很少有现有的深度非参数方法缺乏可扩展性，但我们通过第一个报告这种方法在Imagenet上的性能来证明自己的能力。我们还通过显示将其假定的K值与地面真相更远的k值相比，尤其是在不平衡数据集中的方法时，我们还表明了推断k的重要性。我们的代码可在https://github.com/bgu-cs-vil/deepdpm上找到。
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by K. When K is unknown, however, using model-selection criteria to choose its optimal value might become computationally expensive, especially in DL as the training process would have to be repeated numerous times. In this work, we bridge this gap by introducing an effective deep-clustering method that does not require knowing the value of K as it infers it during the learning. Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones). While the very few existing deep nonparametric methods lack scalability, we demonstrate ours by being the first to report the performance of such a method on ImageNet. We also demonstrate the importance of inferring K by showing how methods that fix it deteriorate in performance when their assumed K value gets further from the ground-truth one, especially on imbalanced datasets. Our code is available at https://github.com/BGU-CS-VIL/DeepDPM.
