  Clustering using neural networks has recently demonstrated promising
performance in machine learning and computer vision applications. However, the
performance of current approaches is limited either by unsupervised learning or
their dependence on large set of labeled data samples. In this paper, we
propose ClusterNet that uses pairwise semantic constraints from very few
labeled data samples (<5% of total data) and exploits the abundant unlabeled
data to drive the clustering approach. We define a new loss function that uses
pairwise semantic similarity between objects combined with constrained k-means
clustering to efficiently utilize both labeled and unlabeled data in the same
framework. The proposed network uses convolution autoencoder to learn a latent
representation that groups data into k specified clusters, while also learning
the cluster centers simultaneously. We evaluate and compare the performance of
ClusterNet on several datasets and state of the art deep clustering approaches.
