  The problem of learning from positive and unlabeled data (A.K.A. PU learning)
has been studied in a binary (i.e., positive versus negative) classification
setting, where the input data consist of (1) observations from the positive
class and their corresponding labels, (2) unlabeled observations from both
positive and negative classes. Generative Adversarial Networks (GANs) have been
used to reduce the problem to the supervised setting with the advantage that
supervised learning has state-of-the-art accuracy in classification tasks. In
order to generate \textit{pseudo}-negative observations, GANs are trained on
positive and unlabeled observations with a modified loss. Using both positive
and \textit{pseudo}-negative observations leads to a supervised learning
setting. The generation of pseudo-negative observations that are realistic
enough to replace missing negative class samples is a bottleneck for current
GAN-based algorithms. By including an additional classifier into the GAN
architecture, we provide a novel GAN-based approach. In our suggested method,
the GAN discriminator instructs the generator only to produce samples that fall
into the unlabeled data distribution, while a second classifier (observer)
network monitors the GAN training to: (i) prevent the generated samples from
falling into the positive distribution; and (ii) learn the features that are
the key distinction between the positive and negative observations. Experiments
on four image datasets demonstrate that our trained observer network performs
better than existing techniques in discriminating between real unseen positive
and negative samples.
