Learning a set of diverse and representative features from a large set of unlabeled
data has long been an area of active research. We present a method that separates
proposals of potential objects into semantic classes in an unsupervised manner. Our
preliminary results show that different object categories emerge and can later be
retrieved from test images. We propose a differentiable clustering approach which
can be integrated with Deep Neural Networks to learn semantic classes in
end-to-fashion without manual class labeling.