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.

翻译：本研究探讨了在生成对抗网络（GANs）框架下半监督学习（SSL，即利用部分标注数据集进行训练）与开放集识别（OSR，即分类与新颖性检测同步进行）之间的关系。尽管先前尚无研究正式建立SSL与OSR之间的关联，但二者各自的方法存在显著相似性。具体而言，SSL-GAN和OSR-GAN均要求其生成器产生“低质量”样本，用以正则化其分类器网络。我们假设SSL与OSR中对“低质量”样本的定义体现了相同概念并服务于同一目标。更正式地讲，低质量样本位于补空间——即分类器嵌入空间中标注类别边界之间及周围的区域。通过使用补空间中的样本对分类器进行正则化，分类器在SSL任务中实现更好的泛化能力，同时在OSR任务中也能实现对开放空间的泛化。为验证该假设，我们在相同的SSL-OSR实验条件下，将基础性SSL-GAN与最先进的OSR-GAN进行对比。实验结果表明，SSL-GAN与OSR-GAN取得了近乎一致的结果，验证了SSL-OSR关联的存在。随后，为推进这一新兴研究方向，我们通过首次基准测试结果对比了多种SSL-GAN在不同SSL-OSR设置下的表现。SSL-OSR的联合框架必将提升分类器训练的实用性与成本效率，本文亦讨论了进一步的理论与应用研究方向。