We consider the problem of retrieving objects from image data and learning to
classify them into meaningful semantic categories with minimal supervision. To
that end, we propose a fully differentiable unsupervised deep clustering
approach to learn semantic classes in an end-to-end fashion without individual
class labeling using only unlabeled object proposals. The key contributions of
our work are 1) a kmeans clustering objective where the clusters are learned as
parameters of the network and are represented as memory units, and 2)
simultaneously building a feature representation, or embedding, while learning
to cluster it. This approach shows promising results on two popular computer
vision datasets: on CIFAR10 for clustering objects, and on the more complex and
challenging Cityscapes dataset for semantically discovering classes which
visually correspond to cars, people, and bicycles. Currently, the only
supervision provided is segmentation objectness masks, but this method can be
extended to use an unsupervised objectness-based object generation mechanism
which will make the approach completely unsupervised.

采用完全可区分的无监督深度聚类方法，仅使用未标记的对象提议，无需个体分类标签来学习语义类，建立特征表示并同时学习聚类。目前该方法仅提供分割对象性掩膜的监督，但该方法可扩展为使用无监督的目标性生成机制，从而使该方法完全无监督。