  Binary classification (BC) is a practical task that is ubiquitous in
real-world problems, such as distinguishing healthy and unhealthy objects in
biomedical diagnostics and defective and non-defective products in
manufacturing inspections. Nonetheless, fully annotated data are commonly
required to effectively solve this problem, and their collection by domain
experts is a tedious and expensive procedure. In contrast to BC, several
significant semi-supervised learning techniques that heavily rely on stochastic
data augmentation techniques have been devised for solving multi-class
classification. In this study, we demonstrate that the stochastic data
augmentation technique is less suitable for solving typical BC problems because
it can omit crucial features that strictly distinguish between positive and
negative samples. To address this issue, we propose a new learning
representation to solve the BC problem using a few labels with a random k-pair
cross-distance learning mechanism. First, by harnessing a few labeled samples,
the encoder network learns the projection of positive and negative samples in
angular spaces to maximize and minimize their inter-class and intra-class
distances, respectively. Second, the classifier learns to discriminate between
positive and negative samples using on-the-fly labels generated based on the
angular space and labeled samples to solve BC tasks. Extensive experiments were
conducted using four real-world publicly available BC datasets. With few labels
and without any data augmentation techniques, the proposed method outperformed
state-of-the-art semi-supervised and self-supervised learning methods.
Moreover, with 10% labeling, our semi-supervised classifier could obtain
competitive accuracy compared with a fully supervised setting.
