  We introduce a novel end-to-end approach for learning to cluster in the
absence of labeled examples. Our clustering objective is based on optimizing
normalized cuts, a criterion which measures both intra-cluster similarity as
well as inter-cluster dissimilarity. We define a differentiable loss function
equivalent to the expected normalized cuts. Unlike much of the work in
unsupervised deep learning, our trained model directly outputs final cluster
assignments, rather than embeddings that need further processing to be usable.
Our approach generalizes to unseen datasets across a wide variety of domains,
including text, and image. Specifically, we achieve state-of-the-art results on
popular unsupervised clustering benchmarks (e.g., MNIST, Reuters, CIFAR-10, and
CIFAR-100), outperforming the strongest baselines by up to 10.9%. Our
generalization results are superior (by up to 21.9%) to the recent
top-performing clustering approach with the ability to generalize.
