  In this paper, we propose a recurrent framework for Joint Unsupervised
LEarning (JULE) of deep representations and image clusters. In our framework,
successive operations in a clustering algorithm are expressed as steps in a
recurrent process, stacked on top of representations output by a Convolutional
Neural Network (CNN). During training, image clusters and representations are
updated jointly: image clustering is conducted in the forward pass, while
representation learning in the backward pass. Our key idea behind this
framework is that good representations are beneficial to image clustering and
clustering results provide supervisory signals to representation learning. By
integrating two processes into a single model with a unified weighted triplet
loss and optimizing it end-to-end, we can obtain not only more powerful
representations, but also more precise image clusters. Extensive experiments
show that our method outperforms the state-of-the-art on image clustering
across a variety of image datasets. Moreover, the learned representations
generalize well when transferred to other tasks.
