  Recently, Convolutional Neural Networks (CNNs) have shown unprecedented
success in the field of computer vision, especially on challenging image
classification tasks by relying on a universal approach, i.e., training a deep
model on a massive dataset of supervised examples. While unlabeled data are
often an abundant resource, collecting a large set of labeled data, on the
other hand, are very expensive, which often require considerable human efforts.
One way to ease out this is to effectively select and label highly informative
instances from a pool of unlabeled data (i.e., active learning). This paper
proposed a new method of batch-mode active learning, Dual Active Sampling(DAS),
which is based on a simple assumption, if two deep neural networks (DNNs) of
the same structure and trained on the same dataset give significantly different
output for a given sample, then that particular sample should be picked for
additional training. While other state of the art methods in this field usually
require intensive computational power or relying on a complicated structure,
DAS is simpler to implement and, managed to get improved results on Cifar-10
with preferable computational time compared to the core-set method.
