The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier. However, such adversarial samples can be generated only within a very small area around the input data point, which limits the adversarial effectiveness of such samples. To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space. LVAT can generate adversarial samples flexibly, resulting in more adverse effects and thus more effective regularization. The latent space is built by a generative model, and in this paper, we examine two different type of models: variational auto-encoder and normalizing flow, specifically Glow. We evaluated the performance of our method in both supervised and semi-supervised learning scenarios for an image classification task using SVHN and CIFAR-10 datasets. In our evaluation, we found that our method outperforms VAT and other state-of-the-art methods.

本文是我们尝试回答两个问题，涵盖道德和作者资格分析领域的问题。首先，由于用于执行作者身份分析的方法意味着他或她创建的内容可以识别作者，因此我们有兴趣找出作者身份证系统是否有可能正确地将作者归因于作者，如果年来，他们经历了重大的心理过渡。其次，从作者的道德价值观演变的角度来看，我们检查了如果作者归因系统在检测单个作者身份方面遇到困难，这将是什么意思。我们着手使用基于预训练的变压器模型的文本分类器执行二进制作者资格分析任务来回答这些问题，并依靠常规相似性指标来回答这些问题。对于测试套装，我们选择了教育史上的日本教育家和专家Arata Osada的作品，其中一半是在第二次世界大战之前写的书，在1950年代又是一半，在此期间，他进行了转变。政治意见的条款。结果，我们能够确认，在10年以上的时间跨度中，Arata Osada撰写的文本，而分类准确性下降了很大的利润率，并且大大低于其他非虚构的文本作家，预测的信心得分仍然与时间跨度较短的水平相似，这表明分类器在许多情况下被欺骗来决定在多年的时间跨度上写的文本实际上是由两个不同的人编写的，这反过来又使我们相信这种变化会影响作者身份分析，并且历史事件对人的著作中所表达的道德观。

最近已将基于学习的THZ多层成像用于非接触式三维（3D）定位和编码。我们通过实验验证，展示了新兴量子机学习（QML）框架的概念验证演示，以应对深度变化，阴影效应和双面内容识别。