On Deep Unsupervised Active Learning
This addresses the need for more effective sample selection in unsupervised active learning, particularly for handling nonlinear data, though it is incremental as it builds on existing active learning approaches.
The paper tackles the problem of selecting representative samples for labeling in unsupervised active learning by proposing DUAL, a deep neural network framework that learns nonlinear embeddings and selects samples preserving input patterns and cluster structure, achieving superior performance on six datasets compared to state-of-the-art methods.
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.