Unsupervised deep clustering for semantic object retrieval
Abstract
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.
Research Areas
