Despite the remarkable results achieved across multiple fields, modern Deep Neural Networks can still prove to be extremely untrustworthy when operating with data coming from a different distribution compared to the one they were trained on. Such a limitation constitutes a major issue in safety-critical applications, where the reliability of a model is a particularly important factor. The Out Of Distribution (OOD) detection task tackles the aforementioned problem by recognizing whether test-time data samples come from a known or an unknown distribution. In order to obtain acceptable performance, traditional methods usually require a training or fine-tuning stage of the network on the known data, but this may be impractical or even infeasible in many deployment scenarios (e.g.