Unsupervised deep clustering for semantic object retrieval


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.