Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anomaly examples in the training data makes optimization of such networks challenging. Attributed to the adversarial training, performance of such models fluctuates drastically with each training step, making it difficult to halt the training at an optimal point. In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions. For this purpose, we propose a method that utilizes the current state as well as an old state of the same generator to create good and bad quality reconstruction examples. The discriminator is trained on these examples to detect the subtle distortions that are often present in the reconstructions of anomalous data. In addition, we propose an efficient generic criterion to stop the training of our model, ensuring elevated performance. Extensive experiments performed on six datasets across multiple domains including image and video based anomaly detection, medical diagnosis, and network security, have demonstrated excellent performance of our approach.

中文翻译：

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使用伪异常稳定对抗学习的一类新奇检测

最近，已经使用对抗性学习生成器的重建损失和/或鉴别器的分类损失来制定异常分数。训练数据中异常示例的不可用使得此类网络的优化具有挑战性。由于对抗性训练，此类模型的性能在每个训练步骤中都会发生剧烈波动，因此很难在最佳点停止训练。在当前的研究中，我们提出了一个强大的异常检测框架，通过将鉴别器的基本作用从识别真假数据转变为区分好坏质量重建来克服这种不稳定性。以此目的，我们提出了一种方法，该方法利用同一生成器的当前状态和旧状态来创建质量好坏的重建示例。判别器在这些示例上进行训练，以检测异常数据重建中经常出现的细微失真。此外，我们提出了一个有效的通用标准来停止我们模型的训练，确保提高性能。在包括基于图像和视频的异常检测、医学诊断和网络安全在内的多个领域的六个数据集上进行的广泛实验证明了我们方法的出色性能。我们提出了一个有效的通用标准来停止我们的模型的训练，确保提高性能。在包括基于图像和视频的异常检测、医学诊断和网络安全在内的多个领域的六个数据集上进行的广泛实验证明了我们方法的出色性能。我们提出了一个有效的通用标准来停止我们的模型的训练，确保提高性能。在包括基于图像和视频的异常检测、医学诊断和网络安全在内的多个领域的六个数据集上进行的广泛实验证明了我们方法的出色性能。