  Outlier detection (OD) has received continuous research interests due to its
wide applications. With the development of deep learning, increasingly deep OD
algorithms are proposed. Despite the availability of numerous deep OD models,
existing research has reported that the performance of deep models is extremely
sensitive to the configuration of hyperparameters (HPs). However, the selection
of HPs for deep OD models remains a notoriously difficult task due to the lack
of any labels and long list of HPs. In our study. we shed light on an essential
factor, training time, that can introduce significant variation in the
performance of deep model. Even the performance is stable across other HPs,
training time itself can cause a serious HP sensitivity issue. Motivated by
this finding, we are dedicated to formulating a strategy to terminate model
training at the optimal iteration. Specifically, we propose a novel metric
called loss entropy to internally evaluate the model performance during
training while an automated training stopping algorithm is devised. To our
knowledge, our approach is the first to enable reliable identification of the
optimal training iteration during training without requiring any labels. Our
experiments on tabular, image datasets show that our approach can be applied to
diverse deep models and datasets. It not only enhances the robustness of deep
models to their HPs, but also improves the performance and reduces plenty of
training time compared to naive training.
