  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.
