  Recently deep learning has been successfully applied to unsupervised active
learning. However, current method attempts to learn a nonlinear transformation
via an auto-encoder while ignoring the sample relation, leaving huge room to
design more effective representation learning mechanisms for unsupervised
active learning. In this paper, we propose a novel deep unsupervised Active
Learning model via Learnable Graphs, named ALLG. ALLG benefits from learning
optimal graph structures to acquire better sample representation and select
representative samples. To make the learnt graph structure more stable and
effective, we take into account $k$-nearest neighbor graph as a priori, and
learn a relation propagation graph structure. We also incorporate shortcut
connections among different layers, which can alleviate the well-known
over-smoothing problem to some extent. To the best of our knowledge, this is
the first attempt to leverage graph structure learning for unsupervised active
learning. Extensive experiments performed on six datasets demonstrate the
efficacy of our method.
