  This study investigates the relationship between semi-supervised learning
(SSL, which is training off partially labelled datasets) and open-set
recognition (OSR, which is classification with simultaneous novelty detection)
under the context of generative adversarial networks (GANs). Although no
previous study has formally linked SSL and OSR, their respective methods share
striking similarities. Specifically, SSL-GANs and OSR-GANs require their
generators to produce 'bad-looking' samples which are used to regularise their
classifier networks. We hypothesise that the definitions of bad-looking samples
in SSL and OSR represents the same concept and realises the same goal. More
formally, bad-looking samples lie in the complementary space, which is the area
between and around the boundaries of the labelled categories within the
classifier's embedding space. By regularising a classifier with samples in the
complementary space, classifiers achieve improved generalisation for SSL and
also generalise the open space for OSR. To test this hypothesis, we compare a
foundational SSL-GAN with the state-of-the-art OSR-GAN under the same SSL-OSR
experimental conditions. Our results find that SSL-GANs achieve near identical
results to OSR-GANs, proving the SSL-OSR link. Subsequently, to further this
new research path, we compare several SSL-GANs various SSL-OSR setups which
this first benchmark results. A combined framework of SSL-OSR certainly
improves the practicality and cost-efficiency of classifier training, and so
further theoretical and application studies are also discussed.
