Ultimately, seven different algorithms exhibiting good performance in distinguishing between the ground-truth and the query dataset are selected. These algorithms range from classical techniques to state-of-the-art neural network-based OCC methods, including: Isolation Forest (IForest), kNN, OCSVM, LOF, Cluster-based LOF (CBLOF), LUNAR, and DeepSVDD. Subsequent evaluations are conducted to choose the best candidate. Except for DeepSVDD, these models are implemented using PyOD , a comprehensive and scalable Python library for identifying outliers in multivariate data. The DeepSVDD architecture is implemented by substituting the convolutional autoencoder with the set-transformer autoencoder in the PyTorch implementation of the original DeepSVDD method.