Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models. Recent wisdom of augmentation methods focuses on generating random pseudo instances that may lead to a mixture of augmented instances with seen anomalies, or out of the typical range of anomalies. To address this issue, we propose a novel saliency-guided data augmentation method, SaliencyCut, to produce pseudo but more common anomalies which tend to stay in the plausible range of anomalies. Furthermore, we deploy a two-head learning strategy consisting of normal and anomaly learning heads, to learn the anomaly score of each sample. Theoretical analyses show that this mechanism offers a more tractable and tighter lower bound of the data log-likelihood. We then design a novel patch-wise residual module in the anomaly learning head to extract and assess the fine-grained anomaly features from each sample, facilitating the learning of discriminative representations of anomaly instances. Extensive experiments conducted on six real-world anomaly detection datasets demonstrate the superiority of our method to competing methods under various settings.

翻译：在开放集场景下的异常检测是一项具有挑战性的任务，需要学习具有判别性的细粒度特征，以检测训练过程中甚至未见过的异常。作为一种廉价且有效的方法，数据增强已被广泛用于生成伪异常，以更好地训练此类模型。近年来增强方法的智慧集中于生成随机伪实例，这可能导致增强实例与已见异常混合，或超出异常的典型范围。为解决此问题，我们提出了一种新颖的显著性引导数据增强方法——SaliencyCut，以生成倾向于停留在合理异常范围内的伪异常但更常见的异常。此外，我们部署了一种由正常学习头和异常学习头组成的双头学习策略，以学习每个样本的异常分数。理论分析表明，该机制提供了数据对数似然更易处理且更紧的下界。然后我们在异常学习头中设计了一种新颖的逐块残差模块，以从每个样本中提取和评估细粒度异常特征，从而促进异常实例判别性表征的学习。在六个真实世界异常检测数据集上进行的大量实验表明，我们的方法在各种设置下均优于竞争方法。