标题： 通过学习优化期望归一化割来广义聚类 显示英文标题

标题： Generalized Clustering by Learning to Optimize Expected Normalized Cuts

摘要： 我们介绍了一种新颖的端到端方法，用于在缺乏标注样本的情况下学习聚类。 我们的聚类目标基于优化归一化割，该准则衡量了簇内相似性和簇间差异性。 我们定义了一个与期望归一化割等价的可微损失函数。 与大多数无监督深度学习的工作不同，我们的训练模型直接输出最终的聚类分配，而不是需要进一步处理才能使用的嵌入。 我们的方法可以推广到多种不同领域的未见过的数据集，包括文本和图像。 具体来说，在流行的无监督聚类基准测试（例如MNIST、Reuters、CIFAR-10和CIFAR-100）上，我们取得了最先进的结果，比最强的基线高出多达10.9%。 我们的泛化结果优于最近表现最好的聚类方法（性能高出多达21.9%）。 显示英文摘要

摘要： 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.