  Unsupervised active learning has attracted increasing attention in recent
years, where its goal is to select representative samples in an unsupervised
setting for human annotating. Most existing works are based on shallow linear
models by assuming that each sample can be well approximated by the span (i.e.,
the set of all linear combinations) of certain selected samples, and then take
these selected samples as representative ones to label. However, in practice,
the data do not necessarily conform to linear models, and how to model
nonlinearity of data often becomes the key point to success. In this paper, we
present a novel Deep neural network framework for Unsupervised Active Learning,
called DUAL. DUAL can explicitly learn a nonlinear embedding to map each input
into a latent space through an encoder-decoder architecture, and introduce a
selection block to select representative samples in the the learnt latent
space. In the selection block, DUAL considers to simultaneously preserve the
whole input patterns as well as the cluster structure of data. Extensive
experiments are performed on six publicly available datasets, and experimental
results clearly demonstrate the efficacy of our method, compared with
state-of-the-arts.
