Learning From Positive and Unlabeled Data Using Observer-GAN

TL;DR

This paper introduces Observer-GAN, a novel GAN-based method for positive and unlabeled learning that generates realistic pseudo-negative samples and improves classification accuracy by using an observer network to distinguish positive from negative data.

Contribution

The paper proposes a new GAN architecture with an observer network that enhances pseudo-negative sample generation and improves PU learning performance.

Findings

Observer-GAN outperforms existing methods in discriminating positive and negative samples.

The observer network effectively learns key features distinguishing classes.

Experiments on four image datasets validate the approach's effectiveness.

Abstract

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.