Pros:

• Do not need to scale given values
• If no given distribution fits the data, this is a good option (because you can not use statistical analysis for outliers)
• Few parameters Cons:
• Must use Sklearn
• Visualization of given results is more complex than traditional outlier detection methods
• Training time can be extremely long if it is implemented incorrectly