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.

本文提出了一种名为 DUAL 的新型深度神经网络框架来进行无监督主动学习，该框架可以通过编码器 - 解码器结构明确地学习非线性嵌入将每个输入映射到潜在空间，并引入选择块来在学习的潜在空间中选择代表性样本，实验结果表明 DUAL 与现有方法相比具有明显的优越性。