Anomalous or outlier data contaminate real-world datasets in domains from telecommunications to healthcare; these data can significantly deviate from the norm and require filtering. This motivates the development of robust deployable anomaly detection models for data cleaning or raising alarms in dynamic information processing systems like browsing, spam, or credit card fraud detection. Conversely, anomalies may be the subject of interest, pivoting the investigation from anomaly detection and removal to their active generation and incorporation, as in "fuzz testing" software. In software fuzz testing, invalid or anomalous inputs can expose hidden vulnerabilities as exceptions such as crashes. In some domains, anomaly detection modeling may suffer from training data scarcity that limits predictive potential. In such cases, generating anomalies to populate synthetic training datasets may mitigate data scarcity and class imbalance. In other cases, the nature of potentially anomalous data may be unknown due to the absence of real-world examples or the ineffectiveness of general formulation or conceptualization approaches for devising anomalous criteria. For instance, manufacturing processes often progress through filtration steps to remove anomalous and defective pieces. However, this relies on a definition of anomalous criteria, despite the rarity of defective pieces.