Novelty detection, a widely studied problem in machine learning, is the problem of detecting a novel class of data that has not been previously observed. A common setting for novelty detection is inductive whereby only examples of the negative class are available during training time. Transductive novelty detection on the other hand has only witnessed a recent surge in interest, it not only makes use of the negative class during training but also incorporates the (unlabeled) test set to detect novel examples. Several studies have emerged under the transductive setting umbrella that have demonstrated its advantage over its inductive counterpart. Depending on the assumptions about the data, these methods go by different names (e.g. transductive novelty detection, semi-supervised novelty detection, positive-unlabeled learning, out-of-distribution detection). With the use of generative adversarial networks (GAN), a segment of those studies have adopted a transductive setup in order to learn how to generate examples of the novel class. In this study, we propose TransductGAN, a transductive generative adversarial network that attempts to learn how to generate image examples from both the novel and negative classes by using a mixture of two Gaussians in the latent space. It achieves that by incorporating an adversarial autoencoder with a GAN network, the ability to generate examples of novel data points offers not only a visual representation of novelties, but also overcomes the hurdle faced by many inductive methods of how to tune the model hyperparameters at the decision rule level. Our model has shown superior performance over state-of-the-art inductive and transductive methods. Our study is fully reproducible with the code available publicly.
翻译:机器学习中广泛研究的一个新颖的发现,即新颖的发现,是发现新颖的一类数据的问题,而这种新颖的发现则是以前没有观察到的。新颖的发现是一种常见的发现方式,在培训期间,只有一些负面的新颖的发现方式,而另一方面,它却只是最近引起兴趣的上升,它不仅利用了培训期间的负面类,而且还结合了(未贴标签的)测试,以探测新颖的例子。在转基因环境伞下出现了一些研究,这些研究展示了它相对于感官对应方的优势。根据数据假设,这些方法采用不同的名称(例如转基因的新颖的发现方式,半超前级的新颖的发现方式,半超前级的新颖的新颖的发现式的发现,正标签的学习,超越了分配的检测。由于使用基因对抗网络,这些研究的一部分采用了一种(未贴现的)转基因模型,以便学习如何生成新颖的模型。 我们建议 TransductionGAN的转基因网络, 尝试如何在高级的图像上生成一个GA型模型。