|Fr 14. Juli 2023
|Outlier detection is an important yet challenging task, especially for unlabeled, high-dimensional, datasets. Due to their self-supervised generative nature, Generative Adversarial Networks (GAN) have proven themselves to be one of the most powerful deep learning methods for outlier detection. However, most state-of-the-art GANs for outlier detection share common limitations. Oftentimes we only achieve great results if the model’s hyperparameters are properly tuned or the underlying network structure is adjusted. This optimization is not possible in practice when the data is unlabeled. If not tuned properly, it is not unusual that a state-of-the-art GAN method is outperformed by simpler shallow methods.
We propose using a GAN architecture with feature ensemble learning to address hyperparameter sensibility and architectural dependency. This follows the success of feature ensembling in mitigating these problems inside other areas of Deep Learning. This thesis will study the optimization problem, training, and tuning of feature ensemble GANs in an unsupervised scenario, comparing it to other deep generative methods in a similar setting.