This study investigates the relationship between semi-supervised learning (SSL) and open-set recognition (OSR) in 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 samples in the complementary space. Subsequently, by regularising networks with generated samples, both SSL and OSR classifiers generalize the open space. To demonstrate the connection between SSL and OSR, we theoretically and experimentally compare state-of-the-art SSL-GAN methods with state-of-the-art OSR-GAN methods. Our results indicate that the SSL optimised margin-GANs, which have a stronger foundation in literature, set the new standard for the combined SSL-OSR task and achieves new state-of-other art results in certain general OSR experiments. However, the OSR optimised adversarial reciprocal point (ARP)-GANs still slightly out-performed margin-GANs at other OSR experiments. This result indicates unique insights for the combined optimisation task of SSL-OSR.

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